Merge branch 'master' into develop-agentprocessor

.circleci/config.yml

       - checkout
       - run:
           name: Combine proto files for caching
-          command: cat protobuf-definitions/proto/mlagents/envs/communicator_objects/*.proto > /tmp/proto_deps.txt
+          command: cat protobuf-definitions/proto/mlagents_envs/communicator_objects/*.proto > /tmp/proto_deps.txt
 
       - restore_cache:
           keys:

.pre-commit-config.yaml

     -   id: mypy
         name: mypy-ml-agents
         files: "ml-agents/.*"
-        args: [--ignore-missing-imports, --disallow-incomplete-defs, --namespace-packages]
+        args: [--ignore-missing-imports, --disallow-incomplete-defs]
-        args: [--ignore-missing-imports, --disallow-incomplete-defs,  --namespace-packages]
+        args: [--ignore-missing-imports, --disallow-incomplete-defs]
-        args: [--ignore-missing-imports, --disallow-incomplete-defs,  --namespace-packages]
+        args: [--ignore-missing-imports, --disallow-incomplete-defs]
 
 -   repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v2.4.0

.pylintrc

     # W0703: Catching too general exception Exception
     W0703,
 
-    # E0401: Unable to import...
-    # E0611: No name '...' in module '...'
-    # need to look into these, probably namespace packages
-    E0401, E0611,
+    # E0401: Unable to import... - triggers for external dependencies like numpy
+    E0401,
 
     # This was causing false positives
     # Appears to be https://github.com/PyCQA/pylint/issues/2981

UnitySDK/Assets/ML-Agents/Scripts/Grpc/CommunicatorObjects/AgentAction.cs

 // <auto-generated>
 //     Generated by the protocol buffer compiler.  DO NOT EDIT!
-//     source: mlagents/envs/communicator_objects/agent_action.proto
+//     source: mlagents_envs/communicator_objects/agent_action.proto
 // </auto-generated>
 #pragma warning disable 1591, 0612, 3021
 #region Designer generated code
 using scg = global::System.Collections.Generic;
 namespace MLAgents.CommunicatorObjects {
 
-  /// <summary>Holder for reflection information generated from mlagents/envs/communicator_objects/agent_action.proto</summary>
+  /// <summary>Holder for reflection information generated from mlagents_envs/communicator_objects/agent_action.proto</summary>
-    /// <summary>File descriptor for mlagents/envs/communicator_objects/agent_action.proto</summary>
+    /// <summary>File descriptor for mlagents_envs/communicator_objects/agent_action.proto</summary>
     public static pbr::FileDescriptor Descriptor {
       get { return descriptor; }
     }
       byte[] descriptorData = global::System.Convert.FromBase64String(
           string.Concat(
-            "CjVtbGFnZW50cy9lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL2FnZW50X2Fj",
+            "CjVtbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL2FnZW50X2Fj",
             "dGlvbi5wcm90bxIUY29tbXVuaWNhdG9yX29iamVjdHMiSwoQQWdlbnRBY3Rp",
             "b25Qcm90bxIWCg52ZWN0b3JfYWN0aW9ucxgBIAMoAhINCgV2YWx1ZRgEIAEo",
             "AkoECAIQA0oECAMQBEoECAUQBkIfqgIcTUxBZ2VudHMuQ29tbXVuaWNhdG9y",

UnitySDK/Assets/ML-Agents/Scripts/Grpc/CommunicatorObjects/AgentInfo.cs

 // <auto-generated>
 //     Generated by the protocol buffer compiler.  DO NOT EDIT!
-//     source: mlagents/envs/communicator_objects/agent_info.proto
+//     source: mlagents_envs/communicator_objects/agent_info.proto
 // </auto-generated>
 #pragma warning disable 1591, 0612, 3021
 #region Designer generated code
 using scg = global::System.Collections.Generic;
 namespace MLAgents.CommunicatorObjects {
 
-  /// <summary>Holder for reflection information generated from mlagents/envs/communicator_objects/agent_info.proto</summary>
+  /// <summary>Holder for reflection information generated from mlagents_envs/communicator_objects/agent_info.proto</summary>
-    /// <summary>File descriptor for mlagents/envs/communicator_objects/agent_info.proto</summary>
+    /// <summary>File descriptor for mlagents_envs/communicator_objects/agent_info.proto</summary>
     public static pbr::FileDescriptor Descriptor {
       get { return descriptor; }
     }
       byte[] descriptorData = global::System.Convert.FromBase64String(
           string.Concat(
-            "CjNtbGFnZW50cy9lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL2FnZW50X2lu",
-            "Zm8ucHJvdG8SFGNvbW11bmljYXRvcl9vYmplY3RzGjRtbGFnZW50cy9lbnZz",
+            "CjNtbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL2FnZW50X2lu",
+            "Zm8ucHJvdG8SFGNvbW11bmljYXRvcl9vYmplY3RzGjRtbGFnZW50c19lbnZz",
             "L2NvbW11bmljYXRvcl9vYmplY3RzL29ic2VydmF0aW9uLnByb3RvItEBCg5B",
             "Z2VudEluZm9Qcm90bxIOCgZyZXdhcmQYByABKAISDAoEZG9uZRgIIAEoCBIY",
             "ChBtYXhfc3RlcF9yZWFjaGVkGAkgASgIEgoKAmlkGAogASgFEhMKC2FjdGlv",

UnitySDK/Assets/ML-Agents/Scripts/Grpc/CommunicatorObjects/AgentInfoActionPair.cs

 // <auto-generated>
 //     Generated by the protocol buffer compiler.  DO NOT EDIT!
-//     source: mlagents/envs/communicator_objects/agent_info_action_pair.proto
+//     source: mlagents_envs/communicator_objects/agent_info_action_pair.proto
 // </auto-generated>
 #pragma warning disable 1591, 0612, 3021
 #region Designer generated code
 using scg = global::System.Collections.Generic;
 namespace MLAgents.CommunicatorObjects {
 
-  /// <summary>Holder for reflection information generated from mlagents/envs/communicator_objects/agent_info_action_pair.proto</summary>
+  /// <summary>Holder for reflection information generated from mlagents_envs/communicator_objects/agent_info_action_pair.proto</summary>
-    /// <summary>File descriptor for mlagents/envs/communicator_objects/agent_info_action_pair.proto</summary>
+    /// <summary>File descriptor for mlagents_envs/communicator_objects/agent_info_action_pair.proto</summary>
     public static pbr::FileDescriptor Descriptor {
       get { return descriptor; }
     }
       byte[] descriptorData = global::System.Convert.FromBase64String(
           string.Concat(
-            "Cj9tbGFnZW50cy9lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL2FnZW50X2lu",
+            "Cj9tbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL2FnZW50X2lu",
-            "bGFnZW50cy9lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL2FnZW50X2luZm8u",
-            "cHJvdG8aNW1sYWdlbnRzL2VudnMvY29tbXVuaWNhdG9yX29iamVjdHMvYWdl",
+            "bGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL2FnZW50X2luZm8u",
+            "cHJvdG8aNW1sYWdlbnRzX2VudnMvY29tbXVuaWNhdG9yX29iamVjdHMvYWdl",
             "bnRfYWN0aW9uLnByb3RvIpEBChhBZ2VudEluZm9BY3Rpb25QYWlyUHJvdG8S",
             "OAoKYWdlbnRfaW5mbxgBIAEoCzIkLmNvbW11bmljYXRvcl9vYmplY3RzLkFn",
             "ZW50SW5mb1Byb3RvEjsKC2FjdGlvbl9pbmZvGAIgASgLMiYuY29tbXVuaWNh",

UnitySDK/Assets/ML-Agents/Scripts/Grpc/CommunicatorObjects/BrainParameters.cs

 // <auto-generated>
 //     Generated by the protocol buffer compiler.  DO NOT EDIT!
-//     source: mlagents/envs/communicator_objects/brain_parameters.proto
+//     source: mlagents_envs/communicator_objects/brain_parameters.proto
 // </auto-generated>
 #pragma warning disable 1591, 0612, 3021
 #region Designer generated code
 using scg = global::System.Collections.Generic;
 namespace MLAgents.CommunicatorObjects {
 
-  /// <summary>Holder for reflection information generated from mlagents/envs/communicator_objects/brain_parameters.proto</summary>
+  /// <summary>Holder for reflection information generated from mlagents_envs/communicator_objects/brain_parameters.proto</summary>
-    /// <summary>File descriptor for mlagents/envs/communicator_objects/brain_parameters.proto</summary>
+    /// <summary>File descriptor for mlagents_envs/communicator_objects/brain_parameters.proto</summary>
     public static pbr::FileDescriptor Descriptor {
       get { return descriptor; }
     }
       byte[] descriptorData = global::System.Convert.FromBase64String(
           string.Concat(
-            "CjltbGFnZW50cy9lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL2JyYWluX3Bh",
+            "CjltbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL2JyYWluX3Bh",
-            "cy9lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL3NwYWNlX3R5cGUucHJvdG8i",
+            "c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL3NwYWNlX3R5cGUucHJvdG8i",
             "2QEKFEJyYWluUGFyYW1ldGVyc1Byb3RvEhoKEnZlY3Rvcl9hY3Rpb25fc2l6",
             "ZRgDIAMoBRIiChp2ZWN0b3JfYWN0aW9uX2Rlc2NyaXB0aW9ucxgFIAMoCRJG",
             "Chh2ZWN0b3JfYWN0aW9uX3NwYWNlX3R5cGUYBiABKA4yJC5jb21tdW5pY2F0",

UnitySDK/Assets/ML-Agents/Scripts/Grpc/CommunicatorObjects/Command.cs

 // <auto-generated>
 //     Generated by the protocol buffer compiler.  DO NOT EDIT!
-//     source: mlagents/envs/communicator_objects/command.proto
+//     source: mlagents_envs/communicator_objects/command.proto
 // </auto-generated>
 #pragma warning disable 1591, 0612, 3021
 #region Designer generated code
 using scg = global::System.Collections.Generic;
 namespace MLAgents.CommunicatorObjects {
 
-  /// <summary>Holder for reflection information generated from mlagents/envs/communicator_objects/command.proto</summary>
+  /// <summary>Holder for reflection information generated from mlagents_envs/communicator_objects/command.proto</summary>
-    /// <summary>File descriptor for mlagents/envs/communicator_objects/command.proto</summary>
+    /// <summary>File descriptor for mlagents_envs/communicator_objects/command.proto</summary>
     public static pbr::FileDescriptor Descriptor {
       get { return descriptor; }
     }
       byte[] descriptorData = global::System.Convert.FromBase64String(
           string.Concat(
-            "CjBtbGFnZW50cy9lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL2NvbW1hbmQu",
+            "CjBtbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL2NvbW1hbmQu",
             "cHJvdG8SFGNvbW11bmljYXRvcl9vYmplY3RzKi0KDENvbW1hbmRQcm90bxII",
             "CgRTVEVQEAASCQoFUkVTRVQQARIICgRRVUlUEAJCH6oCHE1MQWdlbnRzLkNv",
             "bW11bmljYXRvck9iamVjdHNiBnByb3RvMw=="));

UnitySDK/Assets/ML-Agents/Scripts/Grpc/CommunicatorObjects/CustomResetParameters.cs

 // <auto-generated>
 //     Generated by the protocol buffer compiler.  DO NOT EDIT!
-//     source: mlagents/envs/communicator_objects/custom_reset_parameters.proto
+//     source: mlagents_envs/communicator_objects/custom_reset_parameters.proto
 // </auto-generated>
 #pragma warning disable 1591, 0612, 3021
 #region Designer generated code
 using scg = global::System.Collections.Generic;
 namespace MLAgents.CommunicatorObjects {
 
-  /// <summary>Holder for reflection information generated from mlagents/envs/communicator_objects/custom_reset_parameters.proto</summary>
+  /// <summary>Holder for reflection information generated from mlagents_envs/communicator_objects/custom_reset_parameters.proto</summary>
-    /// <summary>File descriptor for mlagents/envs/communicator_objects/custom_reset_parameters.proto</summary>
+    /// <summary>File descriptor for mlagents_envs/communicator_objects/custom_reset_parameters.proto</summary>
     public static pbr::FileDescriptor Descriptor {
       get { return descriptor; }
     }
       byte[] descriptorData = global::System.Convert.FromBase64String(
           string.Concat(
-            "CkBtbGFnZW50cy9lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL2N1c3RvbV9y",
+            "CkBtbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL2N1c3RvbV9y",
             "ZXNldF9wYXJhbWV0ZXJzLnByb3RvEhRjb21tdW5pY2F0b3Jfb2JqZWN0cyIc",
             "ChpDdXN0b21SZXNldFBhcmFtZXRlcnNQcm90b0IfqgIcTUxBZ2VudHMuQ29t",
             "bXVuaWNhdG9yT2JqZWN0c2IGcHJvdG8z"));

UnitySDK/Assets/ML-Agents/Scripts/Grpc/CommunicatorObjects/DemonstrationMeta.cs

 // <auto-generated>
 //     Generated by the protocol buffer compiler.  DO NOT EDIT!
-//     source: mlagents/envs/communicator_objects/demonstration_meta.proto
+//     source: mlagents_envs/communicator_objects/demonstration_meta.proto
 // </auto-generated>
 #pragma warning disable 1591, 0612, 3021
 #region Designer generated code
 using scg = global::System.Collections.Generic;
 namespace MLAgents.CommunicatorObjects {
 
-  /// <summary>Holder for reflection information generated from mlagents/envs/communicator_objects/demonstration_meta.proto</summary>
+  /// <summary>Holder for reflection information generated from mlagents_envs/communicator_objects/demonstration_meta.proto</summary>
-    /// <summary>File descriptor for mlagents/envs/communicator_objects/demonstration_meta.proto</summary>
+    /// <summary>File descriptor for mlagents_envs/communicator_objects/demonstration_meta.proto</summary>
     public static pbr::FileDescriptor Descriptor {
       get { return descriptor; }
     }
       byte[] descriptorData = global::System.Convert.FromBase64String(
           string.Concat(
-            "CjttbGFnZW50cy9lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL2RlbW9uc3Ry",
+            "CjttbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL2RlbW9uc3Ry",
             "YXRpb25fbWV0YS5wcm90bxIUY29tbXVuaWNhdG9yX29iamVjdHMijQEKFkRl",
             "bW9uc3RyYXRpb25NZXRhUHJvdG8SEwoLYXBpX3ZlcnNpb24YASABKAUSGgoS",
             "ZGVtb25zdHJhdGlvbl9uYW1lGAIgASgJEhQKDG51bWJlcl9zdGVwcxgDIAEo",

UnitySDK/Assets/ML-Agents/Scripts/Grpc/CommunicatorObjects/EngineConfiguration.cs

 // <auto-generated>
 //     Generated by the protocol buffer compiler.  DO NOT EDIT!
-//     source: mlagents/envs/communicator_objects/engine_configuration.proto
+//     source: mlagents_envs/communicator_objects/engine_configuration.proto
 // </auto-generated>
 #pragma warning disable 1591, 0612, 3021
 #region Designer generated code
 using scg = global::System.Collections.Generic;
 namespace MLAgents.CommunicatorObjects {
 
-  /// <summary>Holder for reflection information generated from mlagents/envs/communicator_objects/engine_configuration.proto</summary>
+  /// <summary>Holder for reflection information generated from mlagents_envs/communicator_objects/engine_configuration.proto</summary>
-    /// <summary>File descriptor for mlagents/envs/communicator_objects/engine_configuration.proto</summary>
+    /// <summary>File descriptor for mlagents_envs/communicator_objects/engine_configuration.proto</summary>
     public static pbr::FileDescriptor Descriptor {
       get { return descriptor; }
     }
       byte[] descriptorData = global::System.Convert.FromBase64String(
           string.Concat(
-            "Cj1tbGFnZW50cy9lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL2VuZ2luZV9j",
+            "Cj1tbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL2VuZ2luZV9j",
             "b25maWd1cmF0aW9uLnByb3RvEhRjb21tdW5pY2F0b3Jfb2JqZWN0cyKVAQoY",
             "RW5naW5lQ29uZmlndXJhdGlvblByb3RvEg0KBXdpZHRoGAEgASgFEg4KBmhl",
             "aWdodBgCIAEoBRIVCg1xdWFsaXR5X2xldmVsGAMgASgFEhIKCnRpbWVfc2Nh",

UnitySDK/Assets/ML-Agents/Scripts/Grpc/CommunicatorObjects/Header.cs

 // <auto-generated>
 //     Generated by the protocol buffer compiler.  DO NOT EDIT!
-//     source: mlagents/envs/communicator_objects/header.proto
+//     source: mlagents_envs/communicator_objects/header.proto
 // </auto-generated>
 #pragma warning disable 1591, 0612, 3021
 #region Designer generated code
 using scg = global::System.Collections.Generic;
 namespace MLAgents.CommunicatorObjects {
 
-  /// <summary>Holder for reflection information generated from mlagents/envs/communicator_objects/header.proto</summary>
+  /// <summary>Holder for reflection information generated from mlagents_envs/communicator_objects/header.proto</summary>
-    /// <summary>File descriptor for mlagents/envs/communicator_objects/header.proto</summary>
+    /// <summary>File descriptor for mlagents_envs/communicator_objects/header.proto</summary>
     public static pbr::FileDescriptor Descriptor {
       get { return descriptor; }
     }
       byte[] descriptorData = global::System.Convert.FromBase64String(
           string.Concat(
-            "Ci9tbGFnZW50cy9lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL2hlYWRlci5w",
+            "Ci9tbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL2hlYWRlci5w",
             "cm90bxIUY29tbXVuaWNhdG9yX29iamVjdHMiLgoLSGVhZGVyUHJvdG8SDgoG",
             "c3RhdHVzGAEgASgFEg8KB21lc3NhZ2UYAiABKAlCH6oCHE1MQWdlbnRzLkNv",
             "bW11bmljYXRvck9iamVjdHNiBnByb3RvMw=="));

UnitySDK/Assets/ML-Agents/Scripts/Grpc/CommunicatorObjects/Observation.cs

 // <auto-generated>
 //     Generated by the protocol buffer compiler.  DO NOT EDIT!
-//     source: mlagents/envs/communicator_objects/observation.proto
+//     source: mlagents_envs/communicator_objects/observation.proto
 // </auto-generated>
 #pragma warning disable 1591, 0612, 3021
 #region Designer generated code
 using scg = global::System.Collections.Generic;
 namespace MLAgents.CommunicatorObjects {
 
-  /// <summary>Holder for reflection information generated from mlagents/envs/communicator_objects/observation.proto</summary>
+  /// <summary>Holder for reflection information generated from mlagents_envs/communicator_objects/observation.proto</summary>
-    /// <summary>File descriptor for mlagents/envs/communicator_objects/observation.proto</summary>
+    /// <summary>File descriptor for mlagents_envs/communicator_objects/observation.proto</summary>
     public static pbr::FileDescriptor Descriptor {
       get { return descriptor; }
     }
       byte[] descriptorData = global::System.Convert.FromBase64String(
           string.Concat(
-            "CjRtbGFnZW50cy9lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL29ic2VydmF0",
+            "CjRtbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL29ic2VydmF0",
             "aW9uLnByb3RvEhRjb21tdW5pY2F0b3Jfb2JqZWN0cyL5AQoQT2JzZXJ2YXRp",
             "b25Qcm90bxINCgVzaGFwZRgBIAMoBRJEChBjb21wcmVzc2lvbl90eXBlGAIg",
             "ASgOMiouY29tbXVuaWNhdG9yX29iamVjdHMuQ29tcHJlc3Npb25UeXBlUHJv",

UnitySDK/Assets/ML-Agents/Scripts/Grpc/CommunicatorObjects/SpaceType.cs

 // <auto-generated>
 //     Generated by the protocol buffer compiler.  DO NOT EDIT!
-//     source: mlagents/envs/communicator_objects/space_type.proto
+//     source: mlagents_envs/communicator_objects/space_type.proto
 // </auto-generated>
 #pragma warning disable 1591, 0612, 3021
 #region Designer generated code
 using scg = global::System.Collections.Generic;
 namespace MLAgents.CommunicatorObjects {
 
-  /// <summary>Holder for reflection information generated from mlagents/envs/communicator_objects/space_type.proto</summary>
+  /// <summary>Holder for reflection information generated from mlagents_envs/communicator_objects/space_type.proto</summary>
-    /// <summary>File descriptor for mlagents/envs/communicator_objects/space_type.proto</summary>
+    /// <summary>File descriptor for mlagents_envs/communicator_objects/space_type.proto</summary>
     public static pbr::FileDescriptor Descriptor {
       get { return descriptor; }
     }
       byte[] descriptorData = global::System.Convert.FromBase64String(
           string.Concat(
-            "CjNtbGFnZW50cy9lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL3NwYWNlX3R5",
+            "CjNtbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL3NwYWNlX3R5",
             "cGUucHJvdG8SFGNvbW11bmljYXRvcl9vYmplY3RzKi4KDlNwYWNlVHlwZVBy",
             "b3RvEgwKCGRpc2NyZXRlEAASDgoKY29udGludW91cxABQh+qAhxNTEFnZW50",
             "cy5Db21tdW5pY2F0b3JPYmplY3RzYgZwcm90bzM="));

UnitySDK/Assets/ML-Agents/Scripts/Grpc/CommunicatorObjects/UnityInput.cs

 // <auto-generated>
 //     Generated by the protocol buffer compiler.  DO NOT EDIT!
-//     source: mlagents/envs/communicator_objects/unity_input.proto
+//     source: mlagents_envs/communicator_objects/unity_input.proto
 // </auto-generated>
 #pragma warning disable 1591, 0612, 3021
 #region Designer generated code
 using scg = global::System.Collections.Generic;
 namespace MLAgents.CommunicatorObjects {
 
-  /// <summary>Holder for reflection information generated from mlagents/envs/communicator_objects/unity_input.proto</summary>
+  /// <summary>Holder for reflection information generated from mlagents_envs/communicator_objects/unity_input.proto</summary>
-    /// <summary>File descriptor for mlagents/envs/communicator_objects/unity_input.proto</summary>
+    /// <summary>File descriptor for mlagents_envs/communicator_objects/unity_input.proto</summary>
     public static pbr::FileDescriptor Descriptor {
       get { return descriptor; }
     }
       byte[] descriptorData = global::System.Convert.FromBase64String(
           string.Concat(
-            "CjRtbGFnZW50cy9lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL3VuaXR5X2lu",
-            "cHV0LnByb3RvEhRjb21tdW5pY2F0b3Jfb2JqZWN0cxo3bWxhZ2VudHMvZW52",
+            "CjRtbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL3VuaXR5X2lu",
+            "cHV0LnByb3RvEhRjb21tdW5pY2F0b3Jfb2JqZWN0cxo3bWxhZ2VudHNfZW52",
-            "bWxhZ2VudHMvZW52cy9jb21tdW5pY2F0b3Jfb2JqZWN0cy91bml0eV9ybF9p",
+            "bWxhZ2VudHNfZW52cy9jb21tdW5pY2F0b3Jfb2JqZWN0cy91bml0eV9ybF9p",
             "bml0aWFsaXphdGlvbl9pbnB1dC5wcm90byKkAQoPVW5pdHlJbnB1dFByb3Rv",
             "EjkKCHJsX2lucHV0GAEgASgLMicuY29tbXVuaWNhdG9yX29iamVjdHMuVW5p",
             "dHlSTElucHV0UHJvdG8SVgoXcmxfaW5pdGlhbGl6YXRpb25faW5wdXQYAiAB",

UnitySDK/Assets/ML-Agents/Scripts/Grpc/CommunicatorObjects/UnityMessage.cs

 // <auto-generated>
 //     Generated by the protocol buffer compiler.  DO NOT EDIT!
-//     source: mlagents/envs/communicator_objects/unity_message.proto
+//     source: mlagents_envs/communicator_objects/unity_message.proto
 // </auto-generated>
 #pragma warning disable 1591, 0612, 3021
 #region Designer generated code
 using scg = global::System.Collections.Generic;
 namespace MLAgents.CommunicatorObjects {
 
-  /// <summary>Holder for reflection information generated from mlagents/envs/communicator_objects/unity_message.proto</summary>
+  /// <summary>Holder for reflection information generated from mlagents_envs/communicator_objects/unity_message.proto</summary>
-    /// <summary>File descriptor for mlagents/envs/communicator_objects/unity_message.proto</summary>
+    /// <summary>File descriptor for mlagents_envs/communicator_objects/unity_message.proto</summary>
     public static pbr::FileDescriptor Descriptor {
       get { return descriptor; }
     }
       byte[] descriptorData = global::System.Convert.FromBase64String(
           string.Concat(
-            "CjZtbGFnZW50cy9lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL3VuaXR5X21l",
-            "c3NhZ2UucHJvdG8SFGNvbW11bmljYXRvcl9vYmplY3RzGjVtbGFnZW50cy9l",
+            "CjZtbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL3VuaXR5X21l",
+            "c3NhZ2UucHJvdG8SFGNvbW11bmljYXRvcl9vYmplY3RzGjVtbGFnZW50c19l",
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-            "dC5wcm90bxovbWxhZ2VudHMvZW52cy9jb21tdW5pY2F0b3Jfb2JqZWN0cy9o",
+            "bWxhZ2VudHNfZW52cy9jb21tdW5pY2F0b3Jfb2JqZWN0cy91bml0eV9pbnB1",
+            "dC5wcm90bxovbWxhZ2VudHNfZW52cy9jb21tdW5pY2F0b3Jfb2JqZWN0cy9o",
             "ZWFkZXIucHJvdG8iwAEKEVVuaXR5TWVzc2FnZVByb3RvEjEKBmhlYWRlchgB",
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             "aXR5X291dHB1dBgCIAEoCzImLmNvbW11bmljYXRvcl9vYmplY3RzLlVuaXR5",

UnitySDK/Assets/ML-Agents/Scripts/Grpc/CommunicatorObjects/UnityOutput.cs

 // <auto-generated>
 //     Generated by the protocol buffer compiler.  DO NOT EDIT!
-//     source: mlagents/envs/communicator_objects/unity_output.proto
+//     source: mlagents_envs/communicator_objects/unity_output.proto
 // </auto-generated>
 #pragma warning disable 1591, 0612, 3021
 #region Designer generated code
 using scg = global::System.Collections.Generic;
 namespace MLAgents.CommunicatorObjects {
 
-  /// <summary>Holder for reflection information generated from mlagents/envs/communicator_objects/unity_output.proto</summary>
+  /// <summary>Holder for reflection information generated from mlagents_envs/communicator_objects/unity_output.proto</summary>
-    /// <summary>File descriptor for mlagents/envs/communicator_objects/unity_output.proto</summary>
+    /// <summary>File descriptor for mlagents_envs/communicator_objects/unity_output.proto</summary>
     public static pbr::FileDescriptor Descriptor {
       get { return descriptor; }
     }
       byte[] descriptorData = global::System.Convert.FromBase64String(
           string.Concat(
-            "CjVtbGFnZW50cy9lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL3VuaXR5X291",
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+            "dHB1dC5wcm90bxIUY29tbXVuaWNhdG9yX29iamVjdHMaOG1sYWdlbnRzX2Vu",
-            "GkdtbGFnZW50cy9lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL3VuaXR5X3Js",
+            "GkdtbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL3VuaXR5X3Js",
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UnitySDK/Assets/ML-Agents/Scripts/Grpc/CommunicatorObjects/UnityRlInitializationInput.cs

 // <auto-generated>
 //     Generated by the protocol buffer compiler.  DO NOT EDIT!
-//     source: mlagents/envs/communicator_objects/unity_rl_initialization_input.proto
+//     source: mlagents_envs/communicator_objects/unity_rl_initialization_input.proto
 // </auto-generated>
 #pragma warning disable 1591, 0612, 3021
 #region Designer generated code
 using scg = global::System.Collections.Generic;
 namespace MLAgents.CommunicatorObjects {
 
-  /// <summary>Holder for reflection information generated from mlagents/envs/communicator_objects/unity_rl_initialization_input.proto</summary>
+  /// <summary>Holder for reflection information generated from mlagents_envs/communicator_objects/unity_rl_initialization_input.proto</summary>
-    /// <summary>File descriptor for mlagents/envs/communicator_objects/unity_rl_initialization_input.proto</summary>
+    /// <summary>File descriptor for mlagents_envs/communicator_objects/unity_rl_initialization_input.proto</summary>
     public static pbr::FileDescriptor Descriptor {
       get { return descriptor; }
     }
       byte[] descriptorData = global::System.Convert.FromBase64String(
           string.Concat(
-            "CkZtbGFnZW50cy9lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL3VuaXR5X3Js",
+            "CkZtbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL3VuaXR5X3Js",
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             "ZWQYASABKAVCH6oCHE1MQWdlbnRzLkNvbW11bmljYXRvck9iamVjdHNiBnBy",

UnitySDK/Assets/ML-Agents/Scripts/Grpc/CommunicatorObjects/UnityRlInitializationOutput.cs

 // <auto-generated>
 //     Generated by the protocol buffer compiler.  DO NOT EDIT!
-//     source: mlagents/envs/communicator_objects/unity_rl_initialization_output.proto
+//     source: mlagents_envs/communicator_objects/unity_rl_initialization_output.proto
 // </auto-generated>
 #pragma warning disable 1591, 0612, 3021
 #region Designer generated code
 using scg = global::System.Collections.Generic;
 namespace MLAgents.CommunicatorObjects {
 
-  /// <summary>Holder for reflection information generated from mlagents/envs/communicator_objects/unity_rl_initialization_output.proto</summary>
+  /// <summary>Holder for reflection information generated from mlagents_envs/communicator_objects/unity_rl_initialization_output.proto</summary>
-    /// <summary>File descriptor for mlagents/envs/communicator_objects/unity_rl_initialization_output.proto</summary>
+    /// <summary>File descriptor for mlagents_envs/communicator_objects/unity_rl_initialization_output.proto</summary>
     public static pbr::FileDescriptor Descriptor {
       get { return descriptor; }
     }
       byte[] descriptorData = global::System.Convert.FromBase64String(
           string.Concat(
-            "CkdtbGFnZW50cy9lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL3VuaXR5X3Js",
+            "CkdtbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL3VuaXR5X3Js",
-            "amVjdHMaOW1sYWdlbnRzL2VudnMvY29tbXVuaWNhdG9yX29iamVjdHMvYnJh",
+            "amVjdHMaOW1sYWdlbnRzX2VudnMvY29tbXVuaWNhdG9yX29iamVjdHMvYnJh",
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             "CGxvZ19wYXRoGAMgASgJEkQKEGJyYWluX3BhcmFtZXRlcnMYBSADKAsyKi5j",

UnitySDK/Assets/ML-Agents/Scripts/Grpc/CommunicatorObjects/UnityRlInput.cs

 // <auto-generated>
 //     Generated by the protocol buffer compiler.  DO NOT EDIT!
-//     source: mlagents/envs/communicator_objects/unity_rl_input.proto
+//     source: mlagents_envs/communicator_objects/unity_rl_input.proto
 // </auto-generated>
 #pragma warning disable 1591, 0612, 3021
 #region Designer generated code
 using scg = global::System.Collections.Generic;
 namespace MLAgents.CommunicatorObjects {
 
-  /// <summary>Holder for reflection information generated from mlagents/envs/communicator_objects/unity_rl_input.proto</summary>
+  /// <summary>Holder for reflection information generated from mlagents_envs/communicator_objects/unity_rl_input.proto</summary>
-    /// <summary>File descriptor for mlagents/envs/communicator_objects/unity_rl_input.proto</summary>
+    /// <summary>File descriptor for mlagents_envs/communicator_objects/unity_rl_input.proto</summary>
     public static pbr::FileDescriptor Descriptor {
       get { return descriptor; }
     }
       byte[] descriptorData = global::System.Convert.FromBase64String(
           string.Concat(
-            "CjdtbGFnZW50cy9lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL3VuaXR5X3Js",
-            "X2lucHV0LnByb3RvEhRjb21tdW5pY2F0b3Jfb2JqZWN0cxo1bWxhZ2VudHMv",
+            "CjdtbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL3VuaXR5X3Js",
+            "X2lucHV0LnByb3RvEhRjb21tdW5pY2F0b3Jfb2JqZWN0cxo1bWxhZ2VudHNf",
-            "MG1sYWdlbnRzL2VudnMvY29tbXVuaWNhdG9yX29iamVjdHMvY29tbWFuZC5w",
+            "MG1sYWdlbnRzX2VudnMvY29tbXVuaWNhdG9yX29iamVjdHMvY29tbWFuZC5w",
             "cm90byL+AgoRVW5pdHlSTElucHV0UHJvdG8SUAoNYWdlbnRfYWN0aW9ucxgB",
             "IAMoCzI5LmNvbW11bmljYXRvcl9vYmplY3RzLlVuaXR5UkxJbnB1dFByb3Rv",
             "LkFnZW50QWN0aW9uc0VudHJ5EjMKB2NvbW1hbmQYBCABKA4yIi5jb21tdW5p",

UnitySDK/Assets/ML-Agents/Scripts/Grpc/CommunicatorObjects/UnityRlOutput.cs

 // <auto-generated>
 //     Generated by the protocol buffer compiler.  DO NOT EDIT!
-//     source: mlagents/envs/communicator_objects/unity_rl_output.proto
+//     source: mlagents_envs/communicator_objects/unity_rl_output.proto
 // </auto-generated>
 #pragma warning disable 1591, 0612, 3021
 #region Designer generated code
 using scg = global::System.Collections.Generic;
 namespace MLAgents.CommunicatorObjects {
 
-  /// <summary>Holder for reflection information generated from mlagents/envs/communicator_objects/unity_rl_output.proto</summary>
+  /// <summary>Holder for reflection information generated from mlagents_envs/communicator_objects/unity_rl_output.proto</summary>
-    /// <summary>File descriptor for mlagents/envs/communicator_objects/unity_rl_output.proto</summary>
+    /// <summary>File descriptor for mlagents_envs/communicator_objects/unity_rl_output.proto</summary>
     public static pbr::FileDescriptor Descriptor {
       get { return descriptor; }
     }
       byte[] descriptorData = global::System.Convert.FromBase64String(
           string.Concat(
-            "CjhtbGFnZW50cy9lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL3VuaXR5X3Js",
+            "CjhtbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL3VuaXR5X3Js",
-            "L2VudnMvY29tbXVuaWNhdG9yX29iamVjdHMvYWdlbnRfaW5mby5wcm90byK5",
+            "X2VudnMvY29tbXVuaWNhdG9yX29iamVjdHMvYWdlbnRfaW5mby5wcm90byK5",
             "AgoSVW5pdHlSTE91dHB1dFByb3RvEkwKCmFnZW50SW5mb3MYAiADKAsyOC5j",
             "b21tdW5pY2F0b3Jfb2JqZWN0cy5Vbml0eVJMT3V0cHV0UHJvdG8uQWdlbnRJ",
             "bmZvc0VudHJ5EhQKDHNpZGVfY2hhbm5lbBgDIAEoDBpJChJMaXN0QWdlbnRJ",

UnitySDK/Assets/ML-Agents/Scripts/Grpc/CommunicatorObjects/UnityToExternal.cs

 // <auto-generated>
 //     Generated by the protocol buffer compiler.  DO NOT EDIT!
-//     source: mlagents/envs/communicator_objects/unity_to_external.proto
+//     source: mlagents_envs/communicator_objects/unity_to_external.proto
 // </auto-generated>
 #pragma warning disable 1591, 0612, 3021
 #region Designer generated code
 using scg = global::System.Collections.Generic;
 namespace MLAgents.CommunicatorObjects {
 
-  /// <summary>Holder for reflection information generated from mlagents/envs/communicator_objects/unity_to_external.proto</summary>
+  /// <summary>Holder for reflection information generated from mlagents_envs/communicator_objects/unity_to_external.proto</summary>
-    /// <summary>File descriptor for mlagents/envs/communicator_objects/unity_to_external.proto</summary>
+    /// <summary>File descriptor for mlagents_envs/communicator_objects/unity_to_external.proto</summary>
     public static pbr::FileDescriptor Descriptor {
       get { return descriptor; }
     }
       byte[] descriptorData = global::System.Convert.FromBase64String(
           string.Concat(
-            "CjptbGFnZW50cy9lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL3VuaXR5X3Rv",
+            "CjptbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL3VuaXR5X3Rv",
-            "dHMvZW52cy9jb21tdW5pY2F0b3Jfb2JqZWN0cy91bml0eV9tZXNzYWdlLnBy",
+            "dHNfZW52cy9jb21tdW5pY2F0b3Jfb2JqZWN0cy91bml0eV9tZXNzYWdlLnBy",
             "b3RvMnYKFFVuaXR5VG9FeHRlcm5hbFByb3RvEl4KCEV4Y2hhbmdlEicuY29t",
             "bXVuaWNhdG9yX29iamVjdHMuVW5pdHlNZXNzYWdlUHJvdG8aJy5jb21tdW5p",
             "Y2F0b3Jfb2JqZWN0cy5Vbml0eU1lc3NhZ2VQcm90byIAQh+qAhxNTEFnZW50",

UnitySDK/Assets/ML-Agents/Scripts/Grpc/CommunicatorObjects/UnityToExternalGrpc.cs

 #if UNITY_EDITOR || UNITY_STANDALONE_WIN || UNITY_STANDALONE_OSX || UNITY_STANDALONE_LINUX
 // <auto-generated>
 //     Generated by the protocol buffer compiler.  DO NOT EDIT!
-//     source: mlagents/envs/communicator_objects/unity_to_external.proto
+//     source: mlagents_envs/communicator_objects/unity_to_external.proto
 // </auto-generated>
 #pragma warning disable 0414, 1591
 #region Designer generated code

docs/Basic-Guide.md

 like this:
 
 ```console
-INFO:mlagents.envs:
+INFO:mlagents_envs:
-INFO:mlagents.envs:Connected new brain:
+INFO:mlagents_envs:Connected new brain:
 Unity brain name: 3DBallLearning
         Number of Visual Observations (per agent): 0
         Vector Observation space size (per agent): 8
         Vector Action descriptions: ,
-INFO:mlagents.envs:Hyperparameters for the PPO Trainer of brain 3DBallLearning:
+INFO:mlagents_envs:Hyperparameters for the PPO Trainer of brain 3DBallLearning:
         batch_size:          64
         beta:                0.001
         buffer_size:         12000

docs/Learning-Environment-Executable.md

 'file_name' of the `UnityEnvironment`. For instance:
 
 ```python
-from mlagents.envs.environment import UnityEnvironment
+from mlagents_envs.environment import UnityEnvironment
 env = UnityEnvironment(file_name=<env_name>)
 ```
 
 CrashReporter: initialized
 Mono path[0] = '/Users/dericp/workspace/ml-agents/3DBall.app/Contents/Resources/Data/Managed'
 Mono config path = '/Users/dericp/workspace/ml-agents/3DBall.app/Contents/MonoBleedingEdge/etc'
-INFO:mlagents.envs:
+INFO:mlagents_envs:
-INFO:mlagents.envs:Connected new brain:
+INFO:mlagents_envs:Connected new brain:
 Unity brain name: Ball3DLearning
         Number of Visual Observations (per agent): 0
         Vector Observation space size (per agent): 8
         Vector Action descriptions: ,
-INFO:mlagents.envs:Hyperparameters for the PPO Trainer of brain Ball3DLearning:
+INFO:mlagents_envs:Hyperparameters for the PPO Trainer of brain Ball3DLearning:
         batch_size:          64
         beta:                0.001
         buffer_size:         12000

docs/Migrating.md

 
 # Migrating
 
-## Migrating from master to develop
+## Migrating from 0.12 to latest
+  * `reset()` on the Low-Level Python API no longer takes a `train_mode` argument. To modify the performance/speed of the engine, you must use an `EngineConfigurationChannel`
+  * `reset()` on the Low-Level Python API no longer takes a `config` argument. `UnityEnvironment` no longer has a `reset_parameters` field. To modify float properties in the environment, you must use a `FloatPropertiesChannel`. For more information, refer to the [Low Level Python API documentation](Python-API.md)
-* `reset()` on the Low-Level Python API no longer takes a `train_mode` argument. To modify the performance/speed of the engine, you must use an `EngineConfigurationChannel`
-* `reset()` on the Low-Level Python API no longer takes a `config` argument. `UnityEnvironment` no longer has a `reset_parameters` field. To modify float properties in the environment, you must use a `FloatPropertiesChannel`. For more information, refer to the [Low Level Python API documentation](Python-API.md)
+* `mlagents.envs` was renamed to `mlagents_envs`. The previous repo layout depended on [PEP420](https://www.python.org/dev/peps/pep-0420/), which caused problems with some of our tooling such as mypy and pylint.
+ * Any imports from `mlagents.envs` should be replaced with `mlagents_envs`.
 
 ## Migrating from ML-Agents toolkit v0.11.0 to v0.12.0
 
 * `UnitySDK/Assets/ML-Agents/Scripts/Communicator.cs` and its class `Communicator` have been renamed to `UnitySDK/Assets/ML-Agents/Scripts/ICommunicator.cs` and `ICommunicator` respectively.
 * The `SpaceType` Enums `discrete`, and `continuous` have been renamed to `Discrete` and `Continuous`.
 * We have removed the `Done` call as well as the capacity to set `Max Steps` on the Academy. Therefore an AcademyReset will never be triggered from C# (only from Python). If you want to reset the simulation after a fixed number of steps, or when an event in the simulation occurs, we recommend looking at our multi-agent example environments (such as BananaCollector). In our examples, groups of Agents can be reset through an "Area" that can reset groups of Agents.
-* The import for `mlagents.envs.UnityEnvironment` was removed. If you are using the Python API, change `from mlagents.envs import UnityEnvironment` to `from mlagents.envs.environment import UnityEnvironment`.
+* The import for `mlagents.envs.UnityEnvironment` was removed. If you are using the Python API, change `from mlagents_envs import UnityEnvironment` to `from mlagents_envs.environment import UnityEnvironment`.
 
 
 ## Migrating from ML-Agents toolkit v0.8 to v0.9

docs/Python-API.md

 # Unity ML-Agents Python Low Level API
 
 The `mlagents` Python package contains two components: a low level API which
-allows you to interact directly with a Unity Environment (`mlagents.envs`) and
+allows you to interact directly with a Unity Environment (`mlagents_envs`) and
 an entry point to train (`mlagents-learn`) which allows you to train agents in
 Unity Environments using our implementations of reinforcement learning or
 imitation learning.
 
-## mlagents.envs
+## mlagents_envs
 
 The ML-Agents Toolkit Low Level API is a Python API for controlling the simulation
 loop of an environment or game built with Unity. This API is used by the
 - **AgentGroupSpec** — describes the shape of the data inside a BatchedStepResult.
   For example, provides the dimensions of the observations of a group.
 
-These classes are all defined in the [base_env](../ml-agents-envs/mlagents/envs/base_env.py)
+These classes are all defined in the [base_env](../ml-agents-envs/mlagents_envs/base_env.py)
 script.
 
 An Agent Group is a group of Agents identified by a string name that share the same
 ## Loading a Unity Environment
 
 Python-side communication happens through `UnityEnvironment` which is located in
-`ml-agents/mlagents/envs`. To load a Unity environment from a built binary
-file, put the file in the same directory as `envs`. For example, if the filename
-of your Unity environment is 3DBall.app, in python, run:
+[`environment.py`](../ml-agents-envs/mlagents_envs/environment.py). To load
+a Unity environment from a built binary file, put the file in the same directory
+as `envs`. For example, if the filename of your Unity environment is 3DBall.app, in python, run:
-from mlagents.envs.environment import UnityEnvironment
+from mlagents_envs.environment import UnityEnvironment
 env = UnityEnvironment(file_name="3DBall", base_port=5005, seed=1, side_channels=[])
 ```
 
 
 For example :
 ```python
-from mlagents.envs.environment import UnityEnvironment
-from mlagents.envs.side_channel.engine_configuration_channel import EngineConfigurationChannel
+from mlagents_envs.environment import UnityEnvironment
+from mlagents_envs.side_channel.engine_configuration_channel import EngineConfigurationChannel
 
 channel = EngineConfigurationChannel()
 
  * `list_properties` Returns a list of all the string identifiers of the properties
 
 ```python
-from mlagents.envs.environment import UnityEnvironment
-from mlagents.envs.side_channel.float_properties_channel import FloatPropertiesChannel
+from mlagents_envs.environment import UnityEnvironment
+from mlagents_envs.side_channel.float_properties_channel import FloatPropertiesChannel
 
 channel = FloatPropertiesChannel()
 

docs/Training-Generalized-Reinforcement-Learning-Agents.md

 
     * **sub-arguments** - `intervals`
 
-The implementation of the samplers can be found at `ml-agents-envs/mlagents/envs/sampler_class.py`.
+The implementation of the samplers can be found at `ml-agents-envs/mlagents_envs/sampler_class.py`.
 
 ### Defining a New Sampler Type
 

docs/Training-on-Amazon-Web-Service.md

 9. Test the instance setup from Python using:
 
     ```python
-    from mlagents.envs.environment import UnityEnvironment
+    from mlagents_envs.environment import UnityEnvironment
 
     env = UnityEnvironment(<your_env>)
     ```
 Logging to /home/ubuntu/.config/unity3d/<Some_Path>/Player.log
 Traceback (most recent call last):
   File "<stdin>", line 1, in <module>
-  File "/home/ubuntu/ml-agents/ml-agents/mlagents/envs/environment.py", line 63, in __init__
+  File "/home/ubuntu/ml-agents/ml-agents/mlagents_envs/environment.py", line 63, in __init__
-  File "/home/ubuntu/ml-agents/ml-agents/mlagents/envs/environment.py", line 489, in send_academy_parameters
+  File "/home/ubuntu/ml-agents/ml-agents/mlagents_envs/environment.py", line 489, in send_academy_parameters
-  File "/home/ubuntu/ml-agents/ml-agents/mlagents/envs/rpc_communicator.py", line 60, in initialize
-mlagents.envs.exception.UnityTimeOutException: The Unity environment took too long to respond. Make sure that :
+  File "/home/ubuntu/ml-agents/ml-agents/mlagents_envs/rpc_communicator.py", line 60, in initialize
+mlagents_envs.exception.UnityTimeOutException: The Unity environment took too long to respond. Make sure that :
          The environment does not need user interaction to launch
          The environment and the Python interface have compatible versions.
 ```

docs/Training-on-Microsoft-Azure.md

 7. Test the instance setup from Python using:
 
 ```python
-from mlagents.envs.environment import UnityEnvironment
+from mlagents_envs.environment import UnityEnvironment
 
 env = UnityEnvironment(<your_env>)
 ```

gym-unity/gym_unity/__init__.py

-__version__ = "0.12.0"
+__version__ = "0.12.1"

gym-unity/gym_unity/envs/__init__.py

 import itertools
 import gym
 import numpy as np
-from mlagents.envs.environment import UnityEnvironment
+from mlagents_envs.environment import UnityEnvironment
 from gym import error, spaces
 
 

gym-unity/gym_unity/tests/test_gym.py

 
 from gym import spaces
 from gym_unity.envs import UnityEnv, UnityGymException
-from mlagents.envs.base_env import AgentGroupSpec, ActionType, BatchedStepResult
+from mlagents_envs.base_env import AgentGroupSpec, ActionType, BatchedStepResult
 
 
 @mock.patch("gym_unity.envs.UnityEnvironment")

ml-agents-envs/README.md

 
 The `mlagents_envs` Python package contains one sub package:
 
-* `mlagents.envs`: A low level API which allows you to interact directly with a
+* `mlagents_envs`: A low level API which allows you to interact directly with a
   Unity Environment. See
   [here](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Python-API.md)
   for more information on using this package.

ml-agents-envs/setup.py

 import os
 import sys
-from setuptools import setup
+from setuptools import setup, find_packages
-import mlagents.envs
+import mlagents_envs
-VERSION = mlagents.envs.__version__
+VERSION = mlagents_envs.__version__
 
 here = os.path.abspath(os.path.dirname(__file__))
 
         "Programming Language :: Python :: 3.6",
         "Programming Language :: Python :: 3.7",
     ],
-    packages=["mlagents.envs", "mlagents.envs.communicator_objects"],  # Required
+    packages=find_packages(exclude=["*.tests", "*.tests.*", "tests.*", "tests"]),
     zip_safe=False,
     install_requires=[
         "cloudpickle",

ml-agents/README.md

 
 The `mlagents` Python package contains two sub packages:
 
-* `mlagents.envs`: A low level API which allows you to interact directly with a
+* `mlagents_envs`: A low level API which allows you to interact directly with a
   Unity Environment. See
   [here](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Python-API.md)
   for more information on using this package.

ml-agents/mlagents/trainers/__init__.py

-__version__ = "0.12.0"
+__version__ = "0.12.1"

ml-agents/mlagents/trainers/brain.py

 import numpy as np
 import io
 
-from mlagents.envs.communicator_objects.agent_info_pb2 import AgentInfoProto
-from mlagents.envs.communicator_objects.brain_parameters_pb2 import BrainParametersProto
-from mlagents.envs.communicator_objects.observation_pb2 import ObservationProto
-from mlagents.envs.timers import hierarchical_timer, timed
+from mlagents_envs.communicator_objects.agent_info_pb2 import AgentInfoProto
+from mlagents_envs.communicator_objects.brain_parameters_pb2 import BrainParametersProto
+from mlagents_envs.communicator_objects.observation_pb2 import ObservationProto
+from mlagents_envs.timers import hierarchical_timer, timed
-logger = logging.getLogger("mlagents.envs")
+logger = logging.getLogger("mlagents.trainers")
 
 
 class CameraResolution(NamedTuple):

ml-agents/mlagents/trainers/brain_conversion_utils.py

 from mlagents.trainers.brain import BrainInfo, BrainParameters, CameraResolution
-from mlagents.envs.base_env import BatchedStepResult, AgentGroupSpec
-from mlagents.envs.exception import UnityEnvironmentException
+from mlagents_envs.base_env import BatchedStepResult, AgentGroupSpec
+from mlagents_envs.exception import UnityEnvironmentException
 import numpy as np
 from typing import List
 

ml-agents/mlagents/trainers/buffer.py

 import h5py
 from typing import List, BinaryIO
 
-from mlagents.envs.exception import UnityException
+from mlagents_envs.exception import UnityException
 
 
 class BufferException(UnityException):

ml-agents/mlagents/trainers/demo_loader.py

 import numpy as np
 from mlagents.trainers.buffer import AgentBuffer
 from mlagents.trainers.brain import BrainParameters, BrainInfo
-from mlagents.envs.communicator_objects.agent_info_action_pair_pb2 import (
+from mlagents_envs.communicator_objects.agent_info_action_pair_pb2 import (
-from mlagents.envs.communicator_objects.brain_parameters_pb2 import BrainParametersProto
-from mlagents.envs.communicator_objects.demonstration_meta_pb2 import (
+from mlagents_envs.communicator_objects.brain_parameters_pb2 import BrainParametersProto
+from mlagents_envs.communicator_objects.demonstration_meta_pb2 import (
-from mlagents.envs.timers import timed, hierarchical_timer
+from mlagents_envs.timers import timed, hierarchical_timer
 from google.protobuf.internal.decoder import _DecodeVarint32  # type: ignore
 
 

ml-agents/mlagents/trainers/env_manager.py

     brain_name_to_action_info: Dict[str, ActionInfo]
 
     def has_actions_for_brain(self, brain_name: str) -> bool:
-        return (
-            brain_name in self.brain_name_to_action_info
-            and self.brain_name_to_action_info[brain_name].outputs is not None
+        return brain_name in self.brain_name_to_action_info and bool(
+            self.brain_name_to_action_info[brain_name].outputs
         )
 
 

ml-agents/mlagents/trainers/exception.py

     pass
 
 
+class TrainerConfigError(Exception):
+    """
+    Any error related to the configuration of trainers in the ML-Agents Toolkit.
+    """
+
+    pass
+
+
 class CurriculumError(TrainerError):
     """
     Any error related to training with a curriculum.

ml-agents/mlagents/trainers/learn.py

 from typing import Any, Callable, Optional, List, NamedTuple
 
 import mlagents.trainers
-import mlagents.envs
+import mlagents_envs
 from mlagents import tf_utils
 from mlagents.trainers.trainer_controller import TrainerController
 from mlagents.trainers.exception import TrainerError
-from mlagents.envs.environment import UnityEnvironment
+from mlagents_envs.environment import UnityEnvironment
-from mlagents.envs.base_env import BaseEnv
+from mlagents_envs.base_env import BaseEnv
-from mlagents.envs.side_channel.side_channel import SideChannel
-from mlagents.envs.side_channel.engine_configuration_channel import EngineConfig
+from mlagents_envs.side_channel.side_channel import SideChannel
+from mlagents_envs.side_channel.engine_configuration_channel import EngineConfig
 
 
 class CommandLineOptions(NamedTuple):
     # pylint: disable=no-member
     return f""" Version information:
   ml-agents: {mlagents.trainers.__version__},
-  ml-agents-envs: {mlagents.envs.__version__},
+  ml-agents-envs: {mlagents_envs.__version__},
   Communicator API: {UnityEnvironment.API_VERSION},
   TensorFlow: {tf_utils.tf.__version__}"""
 
     print(get_version_string())
     options = parse_command_line()
     trainer_logger = logging.getLogger("mlagents.trainers")
-    env_logger = logging.getLogger("mlagents.envs")
+    env_logger = logging.getLogger("mlagents_envs")
     trainer_logger.info(options)
     if options.debug:
         trainer_logger.setLevel("DEBUG")

ml-agents/mlagents/trainers/meta_curriculum.py

         try:
             for curriculum_filename in os.listdir(curriculum_folder):
                 # This process requires JSON files
-                if not curriculum_filename.lower().endswith(".json"):
+                brain_name, extension = os.path.splitext(curriculum_filename)
+                if extension.lower() != ".json":
-                brain_name = curriculum_filename.split(".")[0]
                 curriculum_filepath = os.path.join(
                     curriculum_folder, curriculum_filename
                 )
         for brain_name, lesson in lesson_nums.items():
             self.brains_to_curriculums[brain_name].lesson_num = lesson
 
-    def _lesson_ready_to_increment(self, brain_name, reward_buff_size):
+    def _lesson_ready_to_increment(
+        self, brain_name: str, reward_buff_size: int
+    ) -> bool:
         """Determines whether the curriculum of a specified brain is ready
         to attempt an increment.
 
             Whether the curriculum of the specified brain should attempt to
             increment its lesson.
         """
+        if brain_name not in self.brains_to_curriculums:
+            return False
+
         return reward_buff_size >= (
             self.brains_to_curriculums[brain_name].min_lesson_length
         )

ml-agents/mlagents/trainers/ppo/multi_gpu_policy.py

 
 from tensorflow.python.client import device_lib
 from mlagents.trainers.brain import BrainParameters
-from mlagents.envs.timers import timed
+from mlagents_envs.timers import timed
 from mlagents.trainers.models import EncoderType, LearningRateSchedule
 from mlagents.trainers.ppo.policy import PPOPolicy
 from mlagents.trainers.ppo.models import PPOModel

ml-agents/mlagents/trainers/ppo/policy.py

 
 from mlagents.tf_utils import tf
 
-from mlagents.envs.timers import timed
+from mlagents_envs.timers import timed
 from mlagents.trainers.brain import BrainParameters
 from mlagents.trainers.models import EncoderType, LearningRateSchedule
 from mlagents.trainers.ppo.models import PPOModel

ml-agents/mlagents/trainers/sac/policy.py

 import numpy as np
 from mlagents.tf_utils import tf
 
-from mlagents.envs.timers import timed
+from mlagents_envs.timers import timed
 from mlagents.trainers.brain import BrainInfo, BrainParameters
 from mlagents.trainers.models import EncoderType, LearningRateSchedule
 from mlagents.trainers.sac.models import SACModel

ml-agents/mlagents/trainers/sac/trainer.py

 
 import numpy as np
 
-from mlagents.envs.timers import timed
+from mlagents_envs.timers import timed
 from mlagents.trainers.sac.policy import SACPolicy
 from mlagents.trainers.rl_trainer import RLTrainer
 from mlagents.trainers.trajectory import Trajectory, SplitObservations

ml-agents/mlagents/trainers/simple_env_manager.py

 from typing import Dict, List
 
-from mlagents.envs.base_env import BaseEnv
+from mlagents_envs.base_env import BaseEnv
-from mlagents.envs.timers import timed
+from mlagents_envs.timers import timed
-from mlagents.envs.side_channel.float_properties_channel import FloatPropertiesChannel
+from mlagents_envs.side_channel.float_properties_channel import FloatPropertiesChannel
 from mlagents.trainers.brain_conversion_utils import (
     step_result_to_brain_info,
     group_spec_to_brain_parameters,

ml-agents/mlagents/trainers/subprocess_env_manager.py

 from typing import Dict, NamedTuple, List, Any, Optional, Callable, Set
 import cloudpickle
 
-from mlagents.envs.environment import UnityEnvironment
-from mlagents.envs.exception import UnityCommunicationException, UnityTimeOutException
+from mlagents_envs.environment import UnityEnvironment
+from mlagents_envs.exception import UnityCommunicationException, UnityTimeOutException
-from mlagents.envs.base_env import BaseEnv
+from mlagents_envs.base_env import BaseEnv
-from mlagents.envs.timers import (
+from mlagents_envs.timers import (
     TimerNode,
     timed,
     hierarchical_timer,
 from mlagents.trainers.brain import AllBrainInfo, BrainParameters
 from mlagents.trainers.action_info import ActionInfo
-from mlagents.envs.side_channel.float_properties_channel import FloatPropertiesChannel
-from mlagents.envs.side_channel.engine_configuration_channel import (
+from mlagents_envs.side_channel.float_properties_channel import FloatPropertiesChannel
+from mlagents_envs.side_channel.engine_configuration_channel import (
-from mlagents.envs.side_channel.side_channel import SideChannel
+from mlagents_envs.side_channel.side_channel import SideChannel
-logger = logging.getLogger("mlagents.envs")
+logger = logging.getLogger("mlagents.trainers")
 
 
 class EnvironmentCommand(NamedTuple):

ml-agents/mlagents/trainers/tests/test_bcmodule.py

 
 
 # Test default values
-@mock.patch("mlagents.envs.environment.UnityEnvironment")
+@mock.patch("mlagents_envs.environment.UnityEnvironment")
 def test_bcmodule_defaults(mock_env):
     # See if default values match
     mock_brain = mb.create_mock_3dball_brain()
 @pytest.mark.parametrize(
     "trainer_config", [ppo_dummy_config(), sac_dummy_config()], ids=["ppo", "sac"]
 )
-@mock.patch("mlagents.envs.environment.UnityEnvironment")
+@mock.patch("mlagents_envs.environment.UnityEnvironment")
 def test_bcmodule_update(mock_env, trainer_config):
     mock_brain = mb.create_mock_3dball_brain()
     env, policy = create_policy_with_bc_mock(
 @pytest.mark.parametrize(
     "trainer_config", [ppo_dummy_config(), sac_dummy_config()], ids=["ppo", "sac"]
 )
-@mock.patch("mlagents.envs.environment.UnityEnvironment")
+@mock.patch("mlagents_envs.environment.UnityEnvironment")
 def test_bcmodule_constant_lr_update(mock_env, trainer_config):
     mock_brain = mb.create_mock_3dball_brain()
     trainer_config["behavioral_cloning"]["steps"] = 0
 @pytest.mark.parametrize(
     "trainer_config", [ppo_dummy_config(), sac_dummy_config()], ids=["ppo", "sac"]
 )
-@mock.patch("mlagents.envs.environment.UnityEnvironment")
+@mock.patch("mlagents_envs.environment.UnityEnvironment")
 def test_bcmodule_rnn_update(mock_env, trainer_config):
     mock_brain = mb.create_mock_3dball_brain()
     env, policy = create_policy_with_bc_mock(
 @pytest.mark.parametrize(
     "trainer_config", [ppo_dummy_config(), sac_dummy_config()], ids=["ppo", "sac"]
 )
-@mock.patch("mlagents.envs.environment.UnityEnvironment")
+@mock.patch("mlagents_envs.environment.UnityEnvironment")
 def test_bcmodule_dc_visual_update(mock_env, trainer_config):
     mock_brain = mb.create_mock_banana_brain()
     env, policy = create_policy_with_bc_mock(
 @pytest.mark.parametrize(
     "trainer_config", [ppo_dummy_config(), sac_dummy_config()], ids=["ppo", "sac"]
 )
-@mock.patch("mlagents.envs.environment.UnityEnvironment")
+@mock.patch("mlagents_envs.environment.UnityEnvironment")
 def test_bcmodule_rnn_dc_update(mock_env, trainer_config):
     mock_brain = mb.create_mock_banana_brain()
     env, policy = create_policy_with_bc_mock(

ml-agents/mlagents/trainers/tests/test_curriculum.py

 from mlagents.trainers.exception import CurriculumConfigError, CurriculumLoadingError
 from mlagents.trainers.curriculum import Curriculum
 
-
 dummy_curriculum_json_str = """
     {
         "measure" : "reward",

ml-agents/mlagents/trainers/tests/test_meta_curriculum.py

 import pytest
-from unittest.mock import patch, call
+from unittest.mock import patch, call, mock_open
+from mlagents.trainers.curriculum import Curriculum
+
+from mlagents.trainers.tests.test_simple_rl import (
+    Simple1DEnvironment,
+    _check_environment_trains,
+    BRAIN_NAME,
+)
+from mlagents.trainers.tests.test_curriculum import dummy_curriculum_json_str
 
 
 class MetaCurriculumTest(MetaCurriculum):
 
 @patch("mlagents.trainers.curriculum.Curriculum.get_config", return_value={})
 @patch("mlagents.trainers.curriculum.Curriculum.__init__", return_value=None)
-@patch("os.listdir", return_value=["Brain1.json", "Brain2.json"])
+@patch("os.listdir", return_value=["Brain1.json", "Brain2.test.json"])
 def test_init_meta_curriculum_happy_path(
     listdir, mock_curriculum_init, mock_curriculum_get_config, default_reset_parameters
 ):
 
     assert "Brain1" in meta_curriculum.brains_to_curriculums
-    assert "Brain2" in meta_curriculum.brains_to_curriculums
+    assert "Brain2.test" in meta_curriculum.brains_to_curriculums
-    calls = [call("test/Brain1.json"), call("test/Brain2.json")]
+    calls = [call("test/Brain1.json"), call("test/Brain2.test.json")]
 
     mock_curriculum_init.assert_has_calls(calls)
 
     new_reset_parameters.update(more_reset_parameters)
 
     assert meta_curriculum.get_config() == new_reset_parameters
+
+
+META_CURRICULUM_CONFIG = """
+    default:
+        trainer: ppo
+        batch_size: 16
+        beta: 5.0e-3
+        buffer_size: 64
+        epsilon: 0.2
+        hidden_units: 128
+        lambd: 0.95
+        learning_rate: 5.0e-3
+        max_steps: 100
+        memory_size: 256
+        normalize: false
+        num_epoch: 3
+        num_layers: 2
+        time_horizon: 64
+        sequence_length: 64
+        summary_freq: 50
+        use_recurrent: false
+        reward_signals:
+            extrinsic:
+                strength: 1.0
+                gamma: 0.99
+    """
+
+
+@pytest.mark.parametrize("curriculum_brain_name", [BRAIN_NAME, "WrongBrainName"])
+def test_simple_metacurriculum(curriculum_brain_name):
+    env = Simple1DEnvironment(use_discrete=False)
+    with patch(
+        "builtins.open", new_callable=mock_open, read_data=dummy_curriculum_json_str
+    ):
+        curriculum = Curriculum("TestBrain.json")
+    mc = MetaCurriculumTest({curriculum_brain_name: curriculum})
+    _check_environment_trains(env, META_CURRICULUM_CONFIG, mc, -100.0)

ml-agents/mlagents/trainers/tests/test_ppo.py

 from mlagents.trainers.ppo.trainer import PPOTrainer, discount_rewards
 from mlagents.trainers.ppo.policy import PPOPolicy
 from mlagents.trainers.brain import BrainParameters
-from mlagents.envs.environment import UnityEnvironment
-from mlagents.envs.mock_communicator import MockCommunicator
+from mlagents_envs.environment import UnityEnvironment
+from mlagents_envs.mock_communicator import MockCommunicator
 from mlagents.trainers.tests import mock_brain as mb
 from mlagents.trainers.tests.mock_brain import make_brain_parameters
 from mlagents.trainers.tests.test_trajectory import make_fake_trajectory
 NUM_AGENTS = 12
 
 
-@mock.patch("mlagents.envs.environment.UnityEnvironment.executable_launcher")
-@mock.patch("mlagents.envs.environment.UnityEnvironment.get_communicator")
+@mock.patch("mlagents_envs.environment.UnityEnvironment.executable_launcher")
+@mock.patch("mlagents_envs.environment.UnityEnvironment.get_communicator")
 def test_ppo_policy_evaluate(mock_communicator, mock_launcher, dummy_config):
     tf.reset_default_graph()
     mock_communicator.return_value = MockCommunicator(
     env.close()
 
 
-@mock.patch("mlagents.envs.environment.UnityEnvironment.executable_launcher")
-@mock.patch("mlagents.envs.environment.UnityEnvironment.get_communicator")
+@mock.patch("mlagents_envs.environment.UnityEnvironment.executable_launcher")
+@mock.patch("mlagents_envs.environment.UnityEnvironment.get_communicator")
 def test_ppo_get_value_estimates(mock_communicator, mock_launcher, dummy_config):
     tf.reset_default_graph()
 
     assert trainer.step == 10
 
 
-@mock.patch("mlagents.envs.environment.UnityEnvironment")
+@mock.patch("mlagents_envs.environment.UnityEnvironment")
 @pytest.mark.parametrize("use_discrete", [True, False])
 def test_trainer_update_policy(mock_env, dummy_config, use_discrete):
     env, mock_brain, _ = mb.setup_mock_env_and_brains(

ml-agents/mlagents/trainers/tests/test_reward_signals.py

 @pytest.mark.parametrize(
     "trainer_config", [ppo_dummy_config(), sac_dummy_config()], ids=["ppo", "sac"]
 )
-@mock.patch("mlagents.envs.environment.UnityEnvironment")
+@mock.patch("mlagents_envs.environment.UnityEnvironment")
 def test_gail_cc(mock_env, trainer_config, gail_dummy_config):
     env, policy = create_policy_mock(
         mock_env, trainer_config, gail_dummy_config, False, False, False
 @pytest.mark.parametrize(
     "trainer_config", [ppo_dummy_config(), sac_dummy_config()], ids=["ppo", "sac"]
 )
-@mock.patch("mlagents.envs.environment.UnityEnvironment")
+@mock.patch("mlagents_envs.environment.UnityEnvironment")
 def test_gail_dc_visual(mock_env, trainer_config, gail_dummy_config):
     gail_dummy_config["gail"]["demo_path"] = (
         os.path.dirname(os.path.abspath(__file__)) + "/testdcvis.demo"
 @pytest.mark.parametrize(
     "trainer_config", [ppo_dummy_config(), sac_dummy_config()], ids=["ppo", "sac"]
 )
-@mock.patch("mlagents.envs.environment.UnityEnvironment")
+@mock.patch("mlagents_envs.environment.UnityEnvironment")
 def test_gail_rnn(mock_env, trainer_config, gail_dummy_config):
     env, policy = create_policy_mock(
         mock_env, trainer_config, gail_dummy_config, True, False, False
 @pytest.mark.parametrize(
     "trainer_config", [ppo_dummy_config(), sac_dummy_config()], ids=["ppo", "sac"]
 )
-@mock.patch("mlagents.envs.environment.UnityEnvironment")
+@mock.patch("mlagents_envs.environment.UnityEnvironment")
 def test_curiosity_cc(mock_env, trainer_config, curiosity_dummy_config):
     env, policy = create_policy_mock(
         mock_env, trainer_config, curiosity_dummy_config, False, False, False
 @pytest.mark.parametrize(
     "trainer_config", [ppo_dummy_config(), sac_dummy_config()], ids=["ppo", "sac"]
 )
-@mock.patch("mlagents.envs.environment.UnityEnvironment")
+@mock.patch("mlagents_envs.environment.UnityEnvironment")
 def test_curiosity_dc(mock_env, trainer_config, curiosity_dummy_config):
     env, policy = create_policy_mock(
         mock_env, trainer_config, curiosity_dummy_config, False, True, False
 @pytest.mark.parametrize(
     "trainer_config", [ppo_dummy_config(), sac_dummy_config()], ids=["ppo", "sac"]
 )
-@mock.patch("mlagents.envs.environment.UnityEnvironment")
+@mock.patch("mlagents_envs.environment.UnityEnvironment")
 def test_curiosity_visual(mock_env, trainer_config, curiosity_dummy_config):
     env, policy = create_policy_mock(
         mock_env, trainer_config, curiosity_dummy_config, False, False, True
 @pytest.mark.parametrize(
     "trainer_config", [ppo_dummy_config(), sac_dummy_config()], ids=["ppo", "sac"]
 )
-@mock.patch("mlagents.envs.environment.UnityEnvironment")
+@mock.patch("mlagents_envs.environment.UnityEnvironment")
 def test_curiosity_rnn(mock_env, trainer_config, curiosity_dummy_config):
     env, policy = create_policy_mock(
         mock_env, trainer_config, curiosity_dummy_config, True, False, False
 @pytest.mark.parametrize(
     "trainer_config", [ppo_dummy_config(), sac_dummy_config()], ids=["ppo", "sac"]
 )
-@mock.patch("mlagents.envs.environment.UnityEnvironment")
+@mock.patch("mlagents_envs.environment.UnityEnvironment")
 def test_extrinsic(mock_env, trainer_config, curiosity_dummy_config):
     env, policy = create_policy_mock(
         mock_env, trainer_config, curiosity_dummy_config, False, False, False

ml-agents/mlagents/trainers/tests/test_sac.py

     return env, policy
 
 
-@mock.patch("mlagents.envs.environment.UnityEnvironment")
+@mock.patch("mlagents_envs.environment.UnityEnvironment")
 def test_sac_cc_policy(mock_env, dummy_config):
     # Test evaluate
     tf.reset_default_graph()
 
 
 @pytest.mark.parametrize("discrete", [True, False], ids=["discrete", "continuous"])
-@mock.patch("mlagents.envs.environment.UnityEnvironment")
+@mock.patch("mlagents_envs.environment.UnityEnvironment")
 def test_sac_update_reward_signals(mock_env, dummy_config, discrete):
     # Test evaluate
     tf.reset_default_graph()
     env.close()
 
 
-@mock.patch("mlagents.envs.environment.UnityEnvironment")
+@mock.patch("mlagents_envs.environment.UnityEnvironment")
 def test_sac_dc_policy(mock_env, dummy_config):
     # Test evaluate
     tf.reset_default_graph()
     env.close()
 
 
-@mock.patch("mlagents.envs.environment.UnityEnvironment")
+@mock.patch("mlagents_envs.environment.UnityEnvironment")
 def test_sac_visual_policy(mock_env, dummy_config):
     # Test evaluate
     tf.reset_default_graph()
     assert type(run_out) is dict
 
 
-@mock.patch("mlagents.envs.environment.UnityEnvironment")
+@mock.patch("mlagents_envs.environment.UnityEnvironment")
 def test_sac_rnn_policy(mock_env, dummy_config):
     # Test evaluate
     tf.reset_default_graph()

ml-agents/mlagents/trainers/tests/test_simple_rl.py

 
 from mlagents.trainers.trainer_controller import TrainerController
 from mlagents.trainers.trainer_util import TrainerFactory
-from mlagents.envs.base_env import (
+from mlagents_envs.base_env import (
     BaseEnv,
     AgentGroupSpec,
     BatchedStepResult,
 from mlagents.trainers.simple_env_manager import SimpleEnvManager
 from mlagents.trainers.sampler_class import SamplerManager
 from mlagents.trainers.stats import StatsReporter
-from mlagents.envs.side_channel.float_properties_channel import FloatPropertiesChannel
+from mlagents_envs.side_channel.float_properties_channel import FloatPropertiesChannel
 
 BRAIN_NAME = __name__
 OBS_SIZE = 1
         pass
 
 
-PPO_CONFIG = """
-    default:
+PPO_CONFIG = f"""
+    {BRAIN_NAME}:
         trainer: ppo
         batch_size: 16
         beta: 5.0e-3
                 gamma: 0.99
     """
 
-SAC_CONFIG = """
-    default:
+SAC_CONFIG = f"""
+    {BRAIN_NAME}:
         trainer: sac
         batch_size: 8
         buffer_size: 500
     """
 
 
-def _check_environment_trains(env, config):
+def _check_environment_trains(
+    env, config, meta_curriculum=None, success_threshold=0.99
+):
     # Create controller and begin training.
     with tempfile.TemporaryDirectory() as dir:
         run_id = "id"
             train_model=True,
             load_model=False,
             seed=seed,
-            meta_curriculum=None,
+            meta_curriculum=meta_curriculum,
             multi_gpu=False,
         )
 
             model_path=dir,
             run_id=run_id,
-            meta_curriculum=None,
+            meta_curriculum=meta_curriculum,
             train=True,
             training_seed=seed,
             sampler_manager=SamplerManager(None),
         print(tc._get_measure_vals())
         for brain_name, mean_reward in tc._get_measure_vals().items():
             assert not math.isnan(mean_reward)
-            assert mean_reward > 0.99
+            assert mean_reward > success_threshold
 
 
 @pytest.mark.parametrize("use_discrete", [True, False])

ml-agents/mlagents/trainers/tests/test_subprocess_env_manager.py

     EnvironmentResponse,
     StepResponse,
 )
-from mlagents.envs.base_env import BaseEnv
-from mlagents.envs.side_channel.engine_configuration_channel import EngineConfig
+from mlagents_envs.base_env import BaseEnv
+from mlagents_envs.side_channel.engine_configuration_channel import EngineConfig
 
 
 def mock_env_factory(worker_id):

ml-agents/mlagents/trainers/tests/test_trainer_util.py

 from mlagents.trainers.trainer_util import load_config, _load_config
 from mlagents.trainers.trainer_metrics import TrainerMetrics
 from mlagents.trainers.ppo.trainer import PPOTrainer
-from mlagents.envs.exception import UnityEnvironmentException
+from mlagents.trainers.exception import TrainerConfigError
+from mlagents.trainers.brain import BrainParameters
 
 
 @pytest.fixture
             use_curiosity: false
             curiosity_strength: 0.0
             curiosity_enc_size: 1
+            reward_signals:
+                extrinsic:
+                    strength: 1.0
+                    gamma: 0.99
         """
     )
 
     BrainParametersMock.return_value.brain_name = "testbrain"
     external_brains = {"testbrain": BrainParametersMock()}
 
-    with pytest.raises(UnityEnvironmentException):
+    with pytest.raises(TrainerConfigError):
         trainer_factory = trainer_util.TrainerFactory(
             trainer_config=bad_config,
             summaries_dir=summaries_dir,
             trainers[brain_name] = trainer_factory.generate(brain_parameters)
 
 
+def test_handles_no_default_section():
+    """
+    Make sure the trainer setup handles a missing "default" in the config.
+    """
+    brain_name = "testbrain"
+    config = dummy_config()
+    no_default_config = {brain_name: config["default"]}
+    brain_parameters = BrainParameters(
+        brain_name=brain_name,
+        vector_observation_space_size=1,
+        camera_resolutions=[],
+        vector_action_space_size=[2],
+        vector_action_descriptions=[],
+        vector_action_space_type=0,
+    )
+
+    trainer_factory = trainer_util.TrainerFactory(
+        trainer_config=no_default_config,
+        summaries_dir="test_dir",
+        run_id="testrun",
+        model_path="model_dir",
+        keep_checkpoints=1,
+        train_model=True,
+        load_model=False,
+        seed=42,
+    )
+    trainer_factory.generate(brain_parameters)
+
+
+def test_raise_if_no_config_for_brain():
+    """
+    Make sure the trainer setup raises a friendlier exception if both "default" and the brain name
+    are missing from the config.
+    """
+    brain_name = "testbrain"
+    config = dummy_config()
+    bad_config = {"some_other_brain": config["default"]}
+    brain_parameters = BrainParameters(
+        brain_name=brain_name,
+        vector_observation_space_size=1,
+        camera_resolutions=[],
+        vector_action_space_size=[2],
+        vector_action_descriptions=[],
+        vector_action_space_type=0,
+    )
+
+    trainer_factory = trainer_util.TrainerFactory(
+        trainer_config=bad_config,
+        summaries_dir="test_dir",
+        run_id="testrun",
+        model_path="model_dir",
+        keep_checkpoints=1,
+        train_model=True,
+        load_model=False,
+        seed=42,
+    )
+    with pytest.raises(TrainerConfigError):
+        trainer_factory.generate(brain_parameters)
+
+
-    with pytest.raises(UnityEnvironmentException):
+    with pytest.raises(TrainerConfigError):
         load_config("thisFileDefinitelyDoesNotExist.yaml")
 
 
 - not
   - parse
     """
-    with pytest.raises(UnityEnvironmentException):
+    with pytest.raises(TrainerConfigError):
         fp = io.StringIO(file_contents)
         _load_config(fp)

ml-agents/mlagents/trainers/tf_policy.py

 import numpy as np
 from mlagents.tf_utils import tf
 
-from mlagents.envs.exception import UnityException
+from mlagents_envs.exception import UnityException
 from mlagents.trainers.policy import Policy
 from mlagents.trainers.action_info import ActionInfo
 from tensorflow.python.platform import gfile

ml-agents/mlagents/trainers/trainer.py

 
 from collections import deque
 
-from mlagents.envs.exception import UnityException
-from mlagents.envs.timers import set_gauge
+from mlagents_envs.exception import UnityException
+from mlagents_envs.timers import set_gauge
 from mlagents.trainers.trainer_metrics import TrainerMetrics
 from mlagents.trainers.tf_policy import TFPolicy
 from mlagents.trainers.stats import StatsReporter
 
 
 class Trainer(object):
-    """This class is the base class for the mlagents.envs.trainers"""
+    """This class is the base class for the mlagents_envs.trainers"""
 
     def __init__(
         self,

ml-agents/mlagents/trainers/trainer_controller.py

 from time import time
 
 from mlagents.trainers.env_manager import EnvManager, EnvironmentStep
-from mlagents.envs.exception import (
+from mlagents_envs.exception import (
-from mlagents.envs.timers import hierarchical_timer, get_timer_tree, timed
+from mlagents_envs.timers import hierarchical_timer, get_timer_tree, timed
 from mlagents.trainers.trainer import Trainer, TrainerMetrics
 from mlagents.trainers.meta_curriculum import MetaCurriculum
 from mlagents.trainers.trainer_util import TrainerFactory
         self.trainer_factory = trainer_factory
         self.model_path = model_path
         self.summaries_dir = summaries_dir
-        self.logger = logging.getLogger("mlagents.envs")
+        self.logger = logging.getLogger("mlagents.trainers")
         self.run_id = run_id
         self.save_freq = save_freq
         self.train_model = train
                 brain_name,
                 curriculum,
             ) in self.meta_curriculum.brains_to_curriculums.items():
+                # Skip brains that are in the metacurriculum but no trainer yet.
+                if brain_name not in self.trainers:
+                    continue
                 if curriculum.measure == "progress":
                     measure_val = (
                         self.trainers[brain_name].get_step

ml-agents/mlagents/trainers/trainer_util.py

 import yaml
 from typing import Any, Dict, TextIO
+import logging
-from mlagents.envs.exception import UnityEnvironmentException
+from mlagents.trainers.exception import TrainerConfigError
+
+logger = logging.getLogger("mlagents.trainers")
 
 
 class TrainerFactory:
     :param multi_gpu: Whether to use multi-GPU training
     :return:
     """
-    trainer_parameters = trainer_config["default"].copy()
+    if "default" not in trainer_config and brain_name not in trainer_config:
+        raise TrainerConfigError(
+            f'Trainer config must have either a "default" section, or a section for the brain name ({brain_name}). '
+            "See config/trainer_config.yaml for an example."
+        )
+
+    trainer_parameters = trainer_config.get("default", {}).copy()
     trainer_parameters["summary_path"] = str(run_id) + "_" + brain_name
     trainer_parameters["model_path"] = "{basedir}/{name}".format(
         basedir=model_path, name=brain_name
             _brain_key = trainer_config[_brain_key]
         trainer_parameters.update(trainer_config[_brain_key])
 
+    min_lesson_length = 1
+    if meta_curriculum:
+        if brain_name in meta_curriculum.brains_to_curriculums:
+            min_lesson_length = meta_curriculum.brains_to_curriculums[
+                brain_name
+            ].min_lesson_length
+        else:
+            logger.warning(
+                f"Metacurriculum enabled, but no curriculum for brain {brain_name}. "
+                f"Brains with curricula: {meta_curriculum.brains_to_curriculums.keys()}. "
+            )
+
-    if trainer_parameters["trainer"] == "offline_bc":
+    if "trainer" not in trainer_parameters:
+        raise TrainerConfigError(
+            f'The "trainer" key must be set in your trainer config for brain {brain_name} (or the default brain).'
+        )
+    trainer_type = trainer_parameters["trainer"]
+
+    if trainer_type == "offline_bc":
-    elif trainer_parameters["trainer"] == "ppo":
+    elif trainer_type == "ppo":
-            meta_curriculum.brains_to_curriculums[brain_name].min_lesson_length
-            if meta_curriculum
-            else 1,
+            min_lesson_length,
             trainer_parameters,
             train_model,
             load_model,
         )
-    elif trainer_parameters["trainer"] == "sac":
+    elif trainer_type == "sac":
-            meta_curriculum.brains_to_curriculums[brain_name].min_lesson_length
-            if meta_curriculum
-            else 1,
+            min_lesson_length,
             trainer_parameters,
             train_model,
             load_model,
     else:
-        raise UnityEnvironmentException(
-            "The trainer config contains "
-            "an unknown trainer type for "
-            "brain {}".format(brain_name)
+        raise TrainerConfigError(
+            f'The trainer config contains an unknown trainer type "{trainer_type}" for brain {brain_name}'
         )
     return trainer
 
         with open(config_path) as data_file:
             return _load_config(data_file)
     except IOError:
-        raise UnityEnvironmentException(
-            f"Config file could not be found at {config_path}."
-        )
+        raise TrainerConfigError(f"Config file could not be found at {config_path}.")
-        raise UnityEnvironmentException(
+        raise TrainerConfigError(
             f"There was an error decoding Config file from {config_path}. "
             f"Make sure your file is save using UTF-8"
         )
     try:
         return yaml.safe_load(fp)
     except yaml.parser.ParserError as e:
-        raise UnityEnvironmentException(
+        raise TrainerConfigError(
             "Error parsing yaml file. Please check for formatting errors. "
             "A tool such as http://www.yamllint.com/ can be helpful with this."
         ) from e

ml-agents/setup.py

 import os
 import sys
 
-from setuptools import setup, find_namespace_packages
+from setuptools import setup, find_packages
 from setuptools.command.install import install
 import mlagents.trainers
 
         "Programming Language :: Python :: 3.7",
     ],
     # find_namespace_packages will recurse through the directories and find all the packages
-    packages=find_namespace_packages(
-        exclude=["*.tests", "*.tests.*", "tests.*", "tests"]
-    ),
+    packages=find_packages(exclude=["*.tests", "*.tests.*", "tests.*", "tests"]),
     zip_safe=False,
     install_requires=[
         # Test-only dependencies should go in test_requirements.txt, not here.

notebooks/getting-started.ipynb

     "import numpy as np\n",
     "import sys\n",
     "\n",
-    "from mlagents.envs.environment import UnityEnvironment\n",
-    "from mlagents.envs.side_channel.engine_configuration_channel import EngineConfig, EngineConfigurationChannel\n",
+    "from mlagents_envs.environment import UnityEnvironment\n",
+    "from mlagents_envs.side_channel.engine_configuration_channel import EngineConfig, EngineConfigurationChannel\n",
     "\n",
     "%matplotlib inline\n",
     "\n",
    "metadata": {},
    "source": [
     "### 5. Take random actions in the environment\n",
-    "Once we restart an environment, we can step the environment forward and provide actions to all of the agents within the environment. Here we simply choose random actions based on the `action_space_type` of the default brain. \n",
+    "Once we restart an environment, we can step the environment forward and provide actions to all of the agents within the environment. Here we simply choose random actions based on the `action_space_type` of the default brain.\n",
     "\n",
     "Once this cell is executed, 10 messages will be printed that detail how much reward will be accumulated for the next 10 episodes. The Unity environment will then pause, waiting for further signals telling it what to do next. Thus, not seeing any animation is expected when running this cell."
    ]

protobuf-definitions/make.sh

 # variables
 
-# GRPC-TOOLS required. Install with `nuget install Grpc.Tools`. 
+# GRPC-TOOLS required. Install with `nuget install Grpc.Tools`.
-SRC_DIR=proto/mlagents/envs/communicator_objects
+SRC_DIR=proto/mlagents_envs/communicator_objects
-PYTHON_PACKAGE=mlagents/envs/communicator_objects
+PYTHON_PACKAGE=mlagents_envs/communicator_objects
 
 # clean
 rm -rf $DST_DIR_C
 $COMPILER/protoc --proto_path=proto --csharp_out=$DST_DIR_C $SRC_DIR/*.proto
 $COMPILER/protoc --proto_path=proto --python_out=$DST_DIR_P --mypy_out=$DST_DIR_P $SRC_DIR/*.proto
 
-# grpc 
+# grpc
-python3 -m grpc_tools.protoc --proto_path=proto --python_out=$DST_DIR_P --grpc_python_out=$DST_DIR_P $SRC_DIR/$GRPC 
+python3 -m grpc_tools.protoc --proto_path=proto --python_out=$DST_DIR_P --grpc_python_out=$DST_DIR_P $SRC_DIR/$GRPC
-do 
+do
 FILE=${FILE##*/}
 # echo from .$(basename $FILE) import \* >> $DST_DIR_P/$PYTHON_PACKAGE/__init__.py
 echo from .${FILE%.py} import \* >> $DST_DIR_P/$PYTHON_PACKAGE/__init__.py

protobuf-definitions/make_for_win.bat

 rem variables
 
-rem GRPC-TOOLS required. Install with `nuget install Grpc.Tools`. 
+rem GRPC-TOOLS required. Install with `nuget install Grpc.Tools`.
-set SRC_DIR=proto\mlagents\envs\communicator_objects
+set SRC_DIR=proto\mlagents_envs\communicator_objects
-set PYTHON_PACKAGE=mlagents\envs\communicator_objects
+set PYTHON_PACKAGE=mlagents_envs\communicator_objects
 
 rem clean
 rd /s /q %DST_DIR_C%
 rem Generate the init file for the python module
 rem rm -f $DST_DIR_P/$PYTHON_PACKAGE/__init__.py
 setlocal enabledelayedexpansion
-for %%i in (%DST_DIR_P%\%PYTHON_PACKAGE%\*.py) do ( 
+for %%i in (%DST_DIR_P%\%PYTHON_PACKAGE%\*.py) do (
 set FILE=%%~ni
 rem echo from .$(basename $FILE) import * >> $DST_DIR_P/$PYTHON_PACKAGE/__init__.py
 echo from .!FILE! import * >> %DST_DIR_P%\%PYTHON_PACKAGE%\__init__.py

utils/validate_versions.py

 
 DIRECTORIES = [
     "ml-agents/mlagents/trainers",
-    "ml-agents-envs/mlagents/envs",
+    "ml-agents-envs/mlagents_envs",
     "gym-unity/gym_unity",
 ]
 

ml-agents-envs/mlagents_envs/tests/test_timers.py

 from unittest import mock
 
-from mlagents.envs import timers
+from mlagents_envs import timers
 
 
 @timers.timed
 
 def test_timers() -> None:
     with mock.patch(
-        "mlagents.envs.timers._global_timer_stack", new_callable=timers.TimerStack
+        "mlagents_envs.timers._global_timer_stack", new_callable=timers.TimerStack
     ) as test_timer:
         # First, run some simple code
         with timers.hierarchical_timer("top_level"):

ml-agents-envs/mlagents_envs/tests/test_side_channel.py

 import struct
-from mlagents.envs.side_channel.side_channel import SideChannel
-from mlagents.envs.side_channel.float_properties_channel import FloatPropertiesChannel
-from mlagents.envs.side_channel.raw_bytes_channel import RawBytesChannel
-from mlagents.envs.environment import UnityEnvironment
+from mlagents_envs.side_channel.side_channel import SideChannel
+from mlagents_envs.side_channel.float_properties_channel import FloatPropertiesChannel
+from mlagents_envs.side_channel.raw_bytes_channel import RawBytesChannel
+from mlagents_envs.environment import UnityEnvironment
 
 
 class IntChannel(SideChannel):

ml-agents-envs/mlagents_envs/tests/test_rpc_utils.py

 from typing import List, Tuple
-from mlagents.envs.communicator_objects.agent_info_pb2 import AgentInfoProto
-from mlagents.envs.communicator_objects.observation_pb2 import (
+from mlagents_envs.communicator_objects.agent_info_pb2 import AgentInfoProto
+from mlagents_envs.communicator_objects.observation_pb2 import (
-from mlagents.envs.communicator_objects.brain_parameters_pb2 import BrainParametersProto
+from mlagents_envs.communicator_objects.brain_parameters_pb2 import BrainParametersProto
-from mlagents.envs.base_env import AgentGroupSpec, ActionType
+from mlagents_envs.base_env import AgentGroupSpec, ActionType
-from mlagents.envs.rpc_utils import (
+from mlagents_envs.rpc_utils import (
     agent_group_spec_from_proto,
     process_pixels,
     _process_visual_observation,

ml-agents-envs/mlagents_envs/tests/test_rpc_communicator.py

 import pytest
 
-from mlagents.envs.rpc_communicator import RpcCommunicator
-from mlagents.envs.exception import UnityWorkerInUseException
+from mlagents_envs.rpc_communicator import RpcCommunicator
+from mlagents_envs.exception import UnityWorkerInUseException
 
 
 def test_rpc_communicator_checks_port_on_create():

ml-agents-envs/mlagents_envs/tests/test_envs.py

 
 import numpy as np
 
-from mlagents.envs.environment import UnityEnvironment
-from mlagents.envs.base_env import BatchedStepResult
-from mlagents.envs.exception import UnityEnvironmentException, UnityActionException
-from mlagents.envs.mock_communicator import MockCommunicator
+from mlagents_envs.environment import UnityEnvironment
+from mlagents_envs.base_env import BatchedStepResult
+from mlagents_envs.exception import UnityEnvironmentException, UnityActionException
+from mlagents_envs.mock_communicator import MockCommunicator
-@mock.patch("mlagents.envs.environment.UnityEnvironment.get_communicator")
+@mock.patch("mlagents_envs.environment.UnityEnvironment.get_communicator")
-@mock.patch("mlagents.envs.environment.UnityEnvironment.executable_launcher")
-@mock.patch("mlagents.envs.environment.UnityEnvironment.get_communicator")
+@mock.patch("mlagents_envs.environment.UnityEnvironment.executable_launcher")
+@mock.patch("mlagents_envs.environment.UnityEnvironment.get_communicator")
 def test_initialization(mock_communicator, mock_launcher):
     mock_communicator.return_value = MockCommunicator(
         discrete_action=False, visual_inputs=0
     env.close()
 
 
-@mock.patch("mlagents.envs.environment.UnityEnvironment.executable_launcher")
-@mock.patch("mlagents.envs.environment.UnityEnvironment.get_communicator")
+@mock.patch("mlagents_envs.environment.UnityEnvironment.executable_launcher")
+@mock.patch("mlagents_envs.environment.UnityEnvironment.get_communicator")
 def test_reset(mock_communicator, mock_launcher):
     mock_communicator.return_value = MockCommunicator(
         discrete_action=False, visual_inputs=0
         assert (n_agents,) + shape == obs.shape
 
 
-@mock.patch("mlagents.envs.environment.UnityEnvironment.executable_launcher")
-@mock.patch("mlagents.envs.environment.UnityEnvironment.get_communicator")
+@mock.patch("mlagents_envs.environment.UnityEnvironment.executable_launcher")
+@mock.patch("mlagents_envs.environment.UnityEnvironment.get_communicator")
 def test_step(mock_communicator, mock_launcher):
     mock_communicator.return_value = MockCommunicator(
         discrete_action=False, visual_inputs=0
     assert batched_step_result.done[2]
 
 
-@mock.patch("mlagents.envs.environment.UnityEnvironment.executable_launcher")
-@mock.patch("mlagents.envs.environment.UnityEnvironment.get_communicator")
+@mock.patch("mlagents_envs.environment.UnityEnvironment.executable_launcher")
+@mock.patch("mlagents_envs.environment.UnityEnvironment.get_communicator")
 def test_close(mock_communicator, mock_launcher):
     comm = MockCommunicator(discrete_action=False, visual_inputs=0)
     mock_communicator.return_value = comm

ml-agents-envs/mlagents_envs/side_channel/raw_bytes_channel.py

-from mlagents.envs.side_channel.side_channel import SideChannel, SideChannelType
+from mlagents_envs.side_channel.side_channel import SideChannel, SideChannelType
 from typing import List
 
 

ml-agents-envs/mlagents_envs/side_channel/float_properties_channel.py

-from mlagents.envs.side_channel.side_channel import SideChannel, SideChannelType
+from mlagents_envs.side_channel.side_channel import SideChannel, SideChannelType
 import struct
 from typing import Dict, Tuple, Optional, List
 

ml-agents-envs/mlagents_envs/side_channel/engine_configuration_channel.py

-from mlagents.envs.side_channel.side_channel import SideChannel, SideChannelType
-from mlagents.envs.exception import UnityCommunicationException
+from mlagents_envs.side_channel.side_channel import SideChannel, SideChannelType
+from mlagents_envs.exception import UnityCommunicationException
 import struct
 from typing import NamedTuple
 

ml-agents-envs/mlagents_envs/rpc_utils.py

-from mlagents.envs.base_env import AgentGroupSpec, ActionType, BatchedStepResult
-from mlagents.envs.timers import hierarchical_timer, timed
-from mlagents.envs.communicator_objects.agent_info_pb2 import AgentInfoProto
-from mlagents.envs.communicator_objects.brain_parameters_pb2 import BrainParametersProto
+from mlagents_envs.base_env import AgentGroupSpec, ActionType, BatchedStepResult
+from mlagents_envs.timers import hierarchical_timer, timed
+from mlagents_envs.communicator_objects.agent_info_pb2 import AgentInfoProto
+from mlagents_envs.communicator_objects.brain_parameters_pb2 import BrainParametersProto
 import logging
 import numpy as np
 import io
-logger = logging.getLogger("mlagents.envs")
+logger = logging.getLogger("mlagents_envs")
 
 
 def agent_group_spec_from_proto(

ml-agents-envs/mlagents_envs/rpc_communicator.py

 from concurrent.futures import ThreadPoolExecutor
 
 from .communicator import Communicator
-from mlagents.envs.communicator_objects.unity_to_external_pb2_grpc import (
+from mlagents_envs.communicator_objects.unity_to_external_pb2_grpc import (
-from mlagents.envs.communicator_objects.unity_message_pb2 import UnityMessageProto
-from mlagents.envs.communicator_objects.unity_input_pb2 import UnityInputProto
-from mlagents.envs.communicator_objects.unity_output_pb2 import UnityOutputProto
+from mlagents_envs.communicator_objects.unity_message_pb2 import UnityMessageProto
+from mlagents_envs.communicator_objects.unity_input_pb2 import UnityInputProto
+from mlagents_envs.communicator_objects.unity_output_pb2 import UnityOutputProto
-logger = logging.getLogger("mlagents.envs")
+logger = logging.getLogger("mlagents_envs")
 
 
 class UnityToExternalServicerImplementation(UnityToExternalProtoServicer):

ml-agents-envs/mlagents_envs/mock_communicator.py

 from .communicator import Communicator
 from .environment import UnityEnvironment
-from mlagents.envs.communicator_objects.unity_rl_output_pb2 import UnityRLOutputProto
-from mlagents.envs.communicator_objects.brain_parameters_pb2 import BrainParametersProto
-from mlagents.envs.communicator_objects.unity_rl_initialization_output_pb2 import (
+from mlagents_envs.communicator_objects.unity_rl_output_pb2 import UnityRLOutputProto
+from mlagents_envs.communicator_objects.brain_parameters_pb2 import BrainParametersProto
+from mlagents_envs.communicator_objects.unity_rl_initialization_output_pb2 import (
-from mlagents.envs.communicator_objects.unity_input_pb2 import UnityInputProto
-from mlagents.envs.communicator_objects.unity_output_pb2 import UnityOutputProto
-from mlagents.envs.communicator_objects.agent_info_pb2 import AgentInfoProto
-from mlagents.envs.communicator_objects.observation_pb2 import (
+from mlagents_envs.communicator_objects.unity_input_pb2 import UnityInputProto
+from mlagents_envs.communicator_objects.unity_output_pb2 import UnityOutputProto
+from mlagents_envs.communicator_objects.agent_info_pb2 import AgentInfoProto
+from mlagents_envs.communicator_objects.observation_pb2 import (
-from mlagents.envs.communicator_objects.space_type_pb2 import discrete, continuous
+from mlagents_envs.communicator_objects.space_type_pb2 import discrete, continuous
 
 
 class MockCommunicator(Communicator):

ml-agents-envs/mlagents_envs/exception.py

 import logging
 
-logger = logging.getLogger("mlagents.envs")
+logger = logging.getLogger("mlagents_envs")
 
 
 class UnityException(Exception):

ml-agents-envs/mlagents_envs/environment.py

 import subprocess
 from typing import Dict, List, Optional, Any
 
-from mlagents.envs.side_channel.side_channel import SideChannel
+from mlagents_envs.side_channel.side_channel import SideChannel
-from mlagents.envs.base_env import (
+from mlagents_envs.base_env import (
     BaseEnv,
     BatchedStepResult,
     AgentGroupSpec,
-from mlagents.envs.timers import timed, hierarchical_timer
-from mlagents.envs.exception import (
+from mlagents_envs.timers import timed, hierarchical_timer
+from mlagents_envs.exception import (
     UnityEnvironmentException,
     UnityCommunicationException,
     UnityActionException,
-from mlagents.envs.communicator_objects.command_pb2 import STEP, RESET
-from mlagents.envs.rpc_utils import (
+from mlagents_envs.communicator_objects.command_pb2 import STEP, RESET
+from mlagents_envs.rpc_utils import (
-from mlagents.envs.communicator_objects.unity_rl_input_pb2 import UnityRLInputProto
-from mlagents.envs.communicator_objects.unity_rl_output_pb2 import UnityRLOutputProto
-from mlagents.envs.communicator_objects.agent_action_pb2 import AgentActionProto
-from mlagents.envs.communicator_objects.unity_output_pb2 import UnityOutputProto
-from mlagents.envs.communicator_objects.unity_rl_initialization_input_pb2 import (
+from mlagents_envs.communicator_objects.unity_rl_input_pb2 import UnityRLInputProto
+from mlagents_envs.communicator_objects.unity_rl_output_pb2 import UnityRLOutputProto
+from mlagents_envs.communicator_objects.agent_action_pb2 import AgentActionProto
+from mlagents_envs.communicator_objects.unity_output_pb2 import UnityOutputProto
+from mlagents_envs.communicator_objects.unity_rl_initialization_input_pb2 import (
-from mlagents.envs.communicator_objects.unity_input_pb2 import UnityInputProto
+from mlagents_envs.communicator_objects.unity_input_pb2 import UnityInputProto
 
 from .rpc_communicator import RpcCommunicator
 from sys import platform
 logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger("mlagents.envs")
+logger = logging.getLogger("mlagents_envs")
 
 
 class UnityEnvironment(BaseEnv):

ml-agents-envs/mlagents_envs/communicator_objects/unity_to_external_pb2_grpc.py

 # Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!
 import grpc
 
-from mlagents.envs.communicator_objects import unity_message_pb2 as mlagents_dot_envs_dot_communicator__objects_dot_unity__message__pb2
+from mlagents_envs.communicator_objects import unity_message_pb2 as mlagents__envs_dot_communicator__objects_dot_unity__message__pb2
 
 
 class UnityToExternalProtoStub(object):
     """
     self.Exchange = channel.unary_unary(
         '/communicator_objects.UnityToExternalProto/Exchange',
-        request_serializer=mlagents_dot_envs_dot_communicator__objects_dot_unity__message__pb2.UnityMessageProto.SerializeToString,
-        response_deserializer=mlagents_dot_envs_dot_communicator__objects_dot_unity__message__pb2.UnityMessageProto.FromString,
+        request_serializer=mlagents__envs_dot_communicator__objects_dot_unity__message__pb2.UnityMessageProto.SerializeToString,
+        response_deserializer=mlagents__envs_dot_communicator__objects_dot_unity__message__pb2.UnityMessageProto.FromString,
         )
 
 
   rpc_method_handlers = {
       'Exchange': grpc.unary_unary_rpc_method_handler(
           servicer.Exchange,
-          request_deserializer=mlagents_dot_envs_dot_communicator__objects_dot_unity__message__pb2.UnityMessageProto.FromString,
-          response_serializer=mlagents_dot_envs_dot_communicator__objects_dot_unity__message__pb2.UnityMessageProto.SerializeToString,
+          request_deserializer=mlagents__envs_dot_communicator__objects_dot_unity__message__pb2.UnityMessageProto.FromString,
+          response_serializer=mlagents__envs_dot_communicator__objects_dot_unity__message__pb2.UnityMessageProto.SerializeToString,
       ),
   }
   generic_handler = grpc.method_handlers_generic_handler(

ml-agents-envs/mlagents_envs/communicator_objects/unity_to_external_pb2.py

 # Generated by the protocol buffer compiler.  DO NOT EDIT!
-# source: mlagents/envs/communicator_objects/unity_to_external.proto
+# source: mlagents_envs/communicator_objects/unity_to_external.proto
 
 import sys
 _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
 _sym_db = _symbol_database.Default()
 
 
-from mlagents.envs.communicator_objects import unity_message_pb2 as mlagents_dot_envs_dot_communicator__objects_dot_unity__message__pb2
+from mlagents_envs.communicator_objects import unity_message_pb2 as mlagents__envs_dot_communicator__objects_dot_unity__message__pb2
-  name='mlagents/envs/communicator_objects/unity_to_external.proto',
+  name='mlagents_envs/communicator_objects/unity_to_external.proto',
-  serialized_pb=_b('\n:mlagents/envs/communicator_objects/unity_to_external.proto\x12\x14\x63ommunicator_objects\x1a\x36mlagents/envs/communicator_objects/unity_message.proto2v\n\x14UnityToExternalProto\x12^\n\x08\x45xchange\x12\'.communicator_objects.UnityMessageProto\x1a\'.communicator_objects.UnityMessageProto\"\x00\x42\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
+  serialized_pb=_b('\n:mlagents_envs/communicator_objects/unity_to_external.proto\x12\x14\x63ommunicator_objects\x1a\x36mlagents_envs/communicator_objects/unity_message.proto2v\n\x14UnityToExternalProto\x12^\n\x08\x45xchange\x12\'.communicator_objects.UnityMessageProto\x1a\'.communicator_objects.UnityMessageProto\"\x00\x42\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
-  dependencies=[mlagents_dot_envs_dot_communicator__objects_dot_unity__message__pb2.DESCRIPTOR,])
+  dependencies=[mlagents__envs_dot_communicator__objects_dot_unity__message__pb2.DESCRIPTOR,])
 
 
 
     full_name='communicator_objects.UnityToExternalProto.Exchange',
     index=0,
     containing_service=None,
-    input_type=mlagents_dot_envs_dot_communicator__objects_dot_unity__message__pb2._UNITYMESSAGEPROTO,
-    output_type=mlagents_dot_envs_dot_communicator__objects_dot_unity__message__pb2._UNITYMESSAGEPROTO,
+    input_type=mlagents__envs_dot_communicator__objects_dot_unity__message__pb2._UNITYMESSAGEPROTO,
+    output_type=mlagents__envs_dot_communicator__objects_dot_unity__message__pb2._UNITYMESSAGEPROTO,
     options=None,
   ),
 ])

ml-agents-envs/mlagents_envs/communicator_objects/unity_rl_output_pb2.pyi

     Message as google___protobuf___message___Message,
 )
 
-from mlagents.envs.communicator_objects.agent_info_pb2 import (
-    AgentInfoProto as mlagents___envs___communicator_objects___agent_info_pb2___AgentInfoProto,
+from mlagents_envs.communicator_objects.agent_info_pb2 import (
+    AgentInfoProto as mlagents_envs___communicator_objects___agent_info_pb2___AgentInfoProto,
 )
 
 from typing import (
         DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
 
         @property
-        def value(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[mlagents___envs___communicator_objects___agent_info_pb2___AgentInfoProto]: ...
+        def value(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[mlagents_envs___communicator_objects___agent_info_pb2___AgentInfoProto]: ...
-            value : typing___Optional[typing___Iterable[mlagents___envs___communicator_objects___agent_info_pb2___AgentInfoProto]] = None,
+            value : typing___Optional[typing___Iterable[mlagents_envs___communicator_objects___agent_info_pb2___AgentInfoProto]] = None,
             ) -> None: ...
         @classmethod
         def FromString(cls, s: builtin___bytes) -> UnityRLOutputProto.ListAgentInfoProto: ...

ml-agents-envs/mlagents_envs/communicator_objects/unity_rl_output_pb2.py

 # Generated by the protocol buffer compiler.  DO NOT EDIT!
-# source: mlagents/envs/communicator_objects/unity_rl_output.proto
+# source: mlagents_envs/communicator_objects/unity_rl_output.proto
 
 import sys
 _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
 _sym_db = _symbol_database.Default()
 
 
-from mlagents.envs.communicator_objects import agent_info_pb2 as mlagents_dot_envs_dot_communicator__objects_dot_agent__info__pb2
+from mlagents_envs.communicator_objects import agent_info_pb2 as mlagents__envs_dot_communicator__objects_dot_agent__info__pb2
-  name='mlagents/envs/communicator_objects/unity_rl_output.proto',
+  name='mlagents_envs/communicator_objects/unity_rl_output.proto',
-  serialized_pb=_b('\n8mlagents/envs/communicator_objects/unity_rl_output.proto\x12\x14\x63ommunicator_objects\x1a\x33mlagents/envs/communicator_objects/agent_info.proto\"\xb9\x02\n\x12UnityRLOutputProto\x12L\n\nagentInfos\x18\x02 \x03(\x0b\x32\x38.communicator_objects.UnityRLOutputProto.AgentInfosEntry\x12\x14\n\x0cside_channel\x18\x03 \x01(\x0c\x1aI\n\x12ListAgentInfoProto\x12\x33\n\x05value\x18\x01 \x03(\x0b\x32$.communicator_objects.AgentInfoProto\x1an\n\x0f\x41gentInfosEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12J\n\x05value\x18\x02 \x01(\x0b\x32;.communicator_objects.UnityRLOutputProto.ListAgentInfoProto:\x02\x38\x01J\x04\x08\x01\x10\x02\x42\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
+  serialized_pb=_b('\n8mlagents_envs/communicator_objects/unity_rl_output.proto\x12\x14\x63ommunicator_objects\x1a\x33mlagents_envs/communicator_objects/agent_info.proto\"\xb9\x02\n\x12UnityRLOutputProto\x12L\n\nagentInfos\x18\x02 \x03(\x0b\x32\x38.communicator_objects.UnityRLOutputProto.AgentInfosEntry\x12\x14\n\x0cside_channel\x18\x03 \x01(\x0c\x1aI\n\x12ListAgentInfoProto\x12\x33\n\x05value\x18\x01 \x03(\x0b\x32$.communicator_objects.AgentInfoProto\x1an\n\x0f\x41gentInfosEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12J\n\x05value\x18\x02 \x01(\x0b\x32;.communicator_objects.UnityRLOutputProto.ListAgentInfoProto:\x02\x38\x01J\x04\x08\x01\x10\x02\x42\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
-  dependencies=[mlagents_dot_envs_dot_communicator__objects_dot_agent__info__pb2.DESCRIPTOR,])
+  dependencies=[mlagents__envs_dot_communicator__objects_dot_agent__info__pb2.DESCRIPTOR,])
 
 
 
   serialized_end=449,
 )
 
-_UNITYRLOUTPUTPROTO_LISTAGENTINFOPROTO.fields_by_name['value'].message_type = mlagents_dot_envs_dot_communicator__objects_dot_agent__info__pb2._AGENTINFOPROTO
+_UNITYRLOUTPUTPROTO_LISTAGENTINFOPROTO.fields_by_name['value'].message_type = mlagents__envs_dot_communicator__objects_dot_agent__info__pb2._AGENTINFOPROTO
 _UNITYRLOUTPUTPROTO_LISTAGENTINFOPROTO.containing_type = _UNITYRLOUTPUTPROTO
 _UNITYRLOUTPUTPROTO_AGENTINFOSENTRY.fields_by_name['value'].message_type = _UNITYRLOUTPUTPROTO_LISTAGENTINFOPROTO
 _UNITYRLOUTPUTPROTO_AGENTINFOSENTRY.containing_type = _UNITYRLOUTPUTPROTO
 
   ListAgentInfoProto = _reflection.GeneratedProtocolMessageType('ListAgentInfoProto', (_message.Message,), dict(
     DESCRIPTOR = _UNITYRLOUTPUTPROTO_LISTAGENTINFOPROTO,
-    __module__ = 'mlagents.envs.communicator_objects.unity_rl_output_pb2'
+    __module__ = 'mlagents_envs.communicator_objects.unity_rl_output_pb2'
     # @@protoc_insertion_point(class_scope:communicator_objects.UnityRLOutputProto.ListAgentInfoProto)
     ))
   ,
-    __module__ = 'mlagents.envs.communicator_objects.unity_rl_output_pb2'
+    __module__ = 'mlagents_envs.communicator_objects.unity_rl_output_pb2'
-  __module__ = 'mlagents.envs.communicator_objects.unity_rl_output_pb2'
+  __module__ = 'mlagents_envs.communicator_objects.unity_rl_output_pb2'
   # @@protoc_insertion_point(class_scope:communicator_objects.UnityRLOutputProto)
   ))
 _sym_db.RegisterMessage(UnityRLOutputProto)

ml-agents-envs/mlagents_envs/communicator_objects/unity_rl_input_pb2.pyi

     Message as google___protobuf___message___Message,
 )
 
-from mlagents.envs.communicator_objects.agent_action_pb2 import (
-    AgentActionProto as mlagents___envs___communicator_objects___agent_action_pb2___AgentActionProto,
+from mlagents_envs.communicator_objects.agent_action_pb2 import (
+    AgentActionProto as mlagents_envs___communicator_objects___agent_action_pb2___AgentActionProto,
-from mlagents.envs.communicator_objects.command_pb2 import (
-    CommandProto as mlagents___envs___communicator_objects___command_pb2___CommandProto,
+from mlagents_envs.communicator_objects.command_pb2 import (
+    CommandProto as mlagents_envs___communicator_objects___command_pb2___CommandProto,
 )
 
 from typing import (
         DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
 
         @property
-        def value(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[mlagents___envs___communicator_objects___agent_action_pb2___AgentActionProto]: ...
+        def value(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[mlagents_envs___communicator_objects___agent_action_pb2___AgentActionProto]: ...
-            value : typing___Optional[typing___Iterable[mlagents___envs___communicator_objects___agent_action_pb2___AgentActionProto]] = None,
+            value : typing___Optional[typing___Iterable[mlagents_envs___communicator_objects___agent_action_pb2___AgentActionProto]] = None,
             ) -> None: ...
         @classmethod
         def FromString(cls, s: builtin___bytes) -> UnityRLInputProto.ListAgentActionProto: ...
             def HasField(self, field_name: typing_extensions___Literal[u"value",b"value"]) -> builtin___bool: ...
             def ClearField(self, field_name: typing_extensions___Literal[u"key",b"key",u"value",b"value"]) -> None: ...
 
-    command = ... # type: mlagents___envs___communicator_objects___command_pb2___CommandProto
+    command = ... # type: mlagents_envs___communicator_objects___command_pb2___CommandProto
     side_channel = ... # type: builtin___bytes
 
     @property
         *,
         agent_actions : typing___Optional[typing___Mapping[typing___Text, UnityRLInputProto.ListAgentActionProto]] = None,
-        command : typing___Optional[mlagents___envs___communicator_objects___command_pb2___CommandProto] = None,
+        command : typing___Optional[mlagents_envs___communicator_objects___command_pb2___CommandProto] = None,
         side_channel : typing___Optional[builtin___bytes] = None,
         ) -> None: ...
     @classmethod

ml-agents-envs/mlagents_envs/communicator_objects/unity_rl_input_pb2.py

 # Generated by the protocol buffer compiler.  DO NOT EDIT!
-# source: mlagents/envs/communicator_objects/unity_rl_input.proto
+# source: mlagents_envs/communicator_objects/unity_rl_input.proto
 
 import sys
 _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
 _sym_db = _symbol_database.Default()
 
 
-from mlagents.envs.communicator_objects import agent_action_pb2 as mlagents_dot_envs_dot_communicator__objects_dot_agent__action__pb2
-from mlagents.envs.communicator_objects import command_pb2 as mlagents_dot_envs_dot_communicator__objects_dot_command__pb2
+from mlagents_envs.communicator_objects import agent_action_pb2 as mlagents__envs_dot_communicator__objects_dot_agent__action__pb2
+from mlagents_envs.communicator_objects import command_pb2 as mlagents__envs_dot_communicator__objects_dot_command__pb2
-  name='mlagents/envs/communicator_objects/unity_rl_input.proto',
+  name='mlagents_envs/communicator_objects/unity_rl_input.proto',
-  serialized_pb=_b('\n7mlagents/envs/communicator_objects/unity_rl_input.proto\x12\x14\x63ommunicator_objects\x1a\x35mlagents/envs/communicator_objects/agent_action.proto\x1a\x30mlagents/envs/communicator_objects/command.proto\"\xfe\x02\n\x11UnityRLInputProto\x12P\n\ragent_actions\x18\x01 \x03(\x0b\x32\x39.communicator_objects.UnityRLInputProto.AgentActionsEntry\x12\x33\n\x07\x63ommand\x18\x04 \x01(\x0e\x32\".communicator_objects.CommandProto\x12\x14\n\x0cside_channel\x18\x05 \x01(\x0c\x1aM\n\x14ListAgentActionProto\x12\x35\n\x05value\x18\x01 \x03(\x0b\x32&.communicator_objects.AgentActionProto\x1aq\n\x11\x41gentActionsEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12K\n\x05value\x18\x02 \x01(\x0b\x32<.communicator_objects.UnityRLInputProto.ListAgentActionProto:\x02\x38\x01J\x04\x08\x02\x10\x03J\x04\x08\x03\x10\x04\x42\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
+  serialized_pb=_b('\n7mlagents_envs/communicator_objects/unity_rl_input.proto\x12\x14\x63ommunicator_objects\x1a\x35mlagents_envs/communicator_objects/agent_action.proto\x1a\x30mlagents_envs/communicator_objects/command.proto\"\xfe\x02\n\x11UnityRLInputProto\x12P\n\ragent_actions\x18\x01 \x03(\x0b\x32\x39.communicator_objects.UnityRLInputProto.AgentActionsEntry\x12\x33\n\x07\x63ommand\x18\x04 \x01(\x0e\x32\".communicator_objects.CommandProto\x12\x14\n\x0cside_channel\x18\x05 \x01(\x0c\x1aM\n\x14ListAgentActionProto\x12\x35\n\x05value\x18\x01 \x03(\x0b\x32&.communicator_objects.AgentActionProto\x1aq\n\x11\x41gentActionsEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12K\n\x05value\x18\x02 \x01(\x0b\x32<.communicator_objects.UnityRLInputProto.ListAgentActionProto:\x02\x38\x01J\x04\x08\x02\x10\x03J\x04\x08\x03\x10\x04\x42\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
-  dependencies=[mlagents_dot_envs_dot_communicator__objects_dot_agent__action__pb2.DESCRIPTOR,mlagents_dot_envs_dot_communicator__objects_dot_command__pb2.DESCRIPTOR,])
+  dependencies=[mlagents__envs_dot_communicator__objects_dot_agent__action__pb2.DESCRIPTOR,mlagents__envs_dot_communicator__objects_dot_command__pb2.DESCRIPTOR,])
 
 
 
   serialized_end=569,
 )
 
-_UNITYRLINPUTPROTO_LISTAGENTACTIONPROTO.fields_by_name['value'].message_type = mlagents_dot_envs_dot_communicator__objects_dot_agent__action__pb2._AGENTACTIONPROTO
+_UNITYRLINPUTPROTO_LISTAGENTACTIONPROTO.fields_by_name['value'].message_type = mlagents__envs_dot_communicator__objects_dot_agent__action__pb2._AGENTACTIONPROTO
-_UNITYRLINPUTPROTO.fields_by_name['command'].enum_type = mlagents_dot_envs_dot_communicator__objects_dot_command__pb2._COMMANDPROTO
+_UNITYRLINPUTPROTO.fields_by_name['command'].enum_type = mlagents__envs_dot_communicator__objects_dot_command__pb2._COMMANDPROTO
 DESCRIPTOR.message_types_by_name['UnityRLInputProto'] = _UNITYRLINPUTPROTO
 _sym_db.RegisterFileDescriptor(DESCRIPTOR)
 
     DESCRIPTOR = _UNITYRLINPUTPROTO_LISTAGENTACTIONPROTO,
-    __module__ = 'mlagents.envs.communicator_objects.unity_rl_input_pb2'
+    __module__ = 'mlagents_envs.communicator_objects.unity_rl_input_pb2'
     # @@protoc_insertion_point(class_scope:communicator_objects.UnityRLInputProto.ListAgentActionProto)
     ))
   ,
-    __module__ = 'mlagents.envs.communicator_objects.unity_rl_input_pb2'
+    __module__ = 'mlagents_envs.communicator_objects.unity_rl_input_pb2'
-  __module__ = 'mlagents.envs.communicator_objects.unity_rl_input_pb2'
+  __module__ = 'mlagents_envs.communicator_objects.unity_rl_input_pb2'
   # @@protoc_insertion_point(class_scope:communicator_objects.UnityRLInputProto)
   ))
 _sym_db.RegisterMessage(UnityRLInputProto)

ml-agents-envs/mlagents_envs/communicator_objects/unity_rl_initialization_output_pb2.pyi

     Message as google___protobuf___message___Message,
 )
 
-from mlagents.envs.communicator_objects.brain_parameters_pb2 import (
-    BrainParametersProto as mlagents___envs___communicator_objects___brain_parameters_pb2___BrainParametersProto,
+from mlagents_envs.communicator_objects.brain_parameters_pb2 import (
+    BrainParametersProto as mlagents_envs___communicator_objects___brain_parameters_pb2___BrainParametersProto,
 )
 
 from typing import (
     log_path = ... # type: typing___Text
 
     @property
-    def brain_parameters(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[mlagents___envs___communicator_objects___brain_parameters_pb2___BrainParametersProto]: ...
+    def brain_parameters(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[mlagents_envs___communicator_objects___brain_parameters_pb2___BrainParametersProto]: ...
 
     def __init__(self,
         *,
-        brain_parameters : typing___Optional[typing___Iterable[mlagents___envs___communicator_objects___brain_parameters_pb2___BrainParametersProto]] = None,
+        brain_parameters : typing___Optional[typing___Iterable[mlagents_envs___communicator_objects___brain_parameters_pb2___BrainParametersProto]] = None,
         ) -> None: ...
     @classmethod
     def FromString(cls, s: builtin___bytes) -> UnityRLInitializationOutputProto: ...

ml-agents-envs/mlagents_envs/communicator_objects/unity_rl_initialization_output_pb2.py

 # Generated by the protocol buffer compiler.  DO NOT EDIT!
-# source: mlagents/envs/communicator_objects/unity_rl_initialization_output.proto
+# source: mlagents_envs/communicator_objects/unity_rl_initialization_output.proto
 
 import sys
 _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
 _sym_db = _symbol_database.Default()
 
 
-from mlagents.envs.communicator_objects import brain_parameters_pb2 as mlagents_dot_envs_dot_communicator__objects_dot_brain__parameters__pb2
+from mlagents_envs.communicator_objects import brain_parameters_pb2 as mlagents__envs_dot_communicator__objects_dot_brain__parameters__pb2
-  name='mlagents/envs/communicator_objects/unity_rl_initialization_output.proto',
+  name='mlagents_envs/communicator_objects/unity_rl_initialization_output.proto',
-  serialized_pb=_b('\nGmlagents/envs/communicator_objects/unity_rl_initialization_output.proto\x12\x14\x63ommunicator_objects\x1a\x39mlagents/envs/communicator_objects/brain_parameters.proto\"\x9f\x01\n UnityRLInitializationOutputProto\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x0f\n\x07version\x18\x02 \x01(\t\x12\x10\n\x08log_path\x18\x03 \x01(\t\x12\x44\n\x10\x62rain_parameters\x18\x05 \x03(\x0b\x32*.communicator_objects.BrainParametersProtoJ\x04\x08\x06\x10\x07\x42\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
+  serialized_pb=_b('\nGmlagents_envs/communicator_objects/unity_rl_initialization_output.proto\x12\x14\x63ommunicator_objects\x1a\x39mlagents_envs/communicator_objects/brain_parameters.proto\"\x9f\x01\n UnityRLInitializationOutputProto\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x0f\n\x07version\x18\x02 \x01(\t\x12\x10\n\x08log_path\x18\x03 \x01(\t\x12\x44\n\x10\x62rain_parameters\x18\x05 \x03(\x0b\x32*.communicator_objects.BrainParametersProtoJ\x04\x08\x06\x10\x07\x42\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
-  dependencies=[mlagents_dot_envs_dot_communicator__objects_dot_brain__parameters__pb2.DESCRIPTOR,])
+  dependencies=[mlagents__envs_dot_communicator__objects_dot_brain__parameters__pb2.DESCRIPTOR,])
 
 
 
   serialized_end=316,
 )
 
-_UNITYRLINITIALIZATIONOUTPUTPROTO.fields_by_name['brain_parameters'].message_type = mlagents_dot_envs_dot_communicator__objects_dot_brain__parameters__pb2._BRAINPARAMETERSPROTO
+_UNITYRLINITIALIZATIONOUTPUTPROTO.fields_by_name['brain_parameters'].message_type = mlagents__envs_dot_communicator__objects_dot_brain__parameters__pb2._BRAINPARAMETERSPROTO
-  __module__ = 'mlagents.envs.communicator_objects.unity_rl_initialization_output_pb2'
+  __module__ = 'mlagents_envs.communicator_objects.unity_rl_initialization_output_pb2'
   # @@protoc_insertion_point(class_scope:communicator_objects.UnityRLInitializationOutputProto)
   ))
 _sym_db.RegisterMessage(UnityRLInitializationOutputProto)

ml-agents-envs/mlagents_envs/communicator_objects/unity_rl_initialization_input_pb2.py

 # Generated by the protocol buffer compiler.  DO NOT EDIT!
-# source: mlagents/envs/communicator_objects/unity_rl_initialization_input.proto
+# source: mlagents_envs/communicator_objects/unity_rl_initialization_input.proto
 
 import sys
 _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
 
 
 DESCRIPTOR = _descriptor.FileDescriptor(
-  name='mlagents/envs/communicator_objects/unity_rl_initialization_input.proto',
+  name='mlagents_envs/communicator_objects/unity_rl_initialization_input.proto',
-  serialized_pb=_b('\nFmlagents/envs/communicator_objects/unity_rl_initialization_input.proto\x12\x14\x63ommunicator_objects\"/\n\x1fUnityRLInitializationInputProto\x12\x0c\n\x04seed\x18\x01 \x01(\x05\x42\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
+  serialized_pb=_b('\nFmlagents_envs/communicator_objects/unity_rl_initialization_input.proto\x12\x14\x63ommunicator_objects\"/\n\x1fUnityRLInitializationInputProto\x12\x0c\n\x04seed\x18\x01 \x01(\x05\x42\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
 )
 
 
 
 UnityRLInitializationInputProto = _reflection.GeneratedProtocolMessageType('UnityRLInitializationInputProto', (_message.Message,), dict(
   DESCRIPTOR = _UNITYRLINITIALIZATIONINPUTPROTO,
-  __module__ = 'mlagents.envs.communicator_objects.unity_rl_initialization_input_pb2'
+  __module__ = 'mlagents_envs.communicator_objects.unity_rl_initialization_input_pb2'
   # @@protoc_insertion_point(class_scope:communicator_objects.UnityRLInitializationInputProto)
   ))
 _sym_db.RegisterMessage(UnityRLInitializationInputProto)

ml-agents-envs/mlagents_envs/communicator_objects/unity_output_pb2.pyi

     Message as google___protobuf___message___Message,
 )
 
-from mlagents.envs.communicator_objects.unity_rl_initialization_output_pb2 import (
-    UnityRLInitializationOutputProto as mlagents___envs___communicator_objects___unity_rl_initialization_output_pb2___UnityRLInitializationOutputProto,
+from mlagents_envs.communicator_objects.unity_rl_initialization_output_pb2 import (
+    UnityRLInitializationOutputProto as mlagents_envs___communicator_objects___unity_rl_initialization_output_pb2___UnityRLInitializationOutputProto,
-from mlagents.envs.communicator_objects.unity_rl_output_pb2 import (
-    UnityRLOutputProto as mlagents___envs___communicator_objects___unity_rl_output_pb2___UnityRLOutputProto,
+from mlagents_envs.communicator_objects.unity_rl_output_pb2 import (
+    UnityRLOutputProto as mlagents_envs___communicator_objects___unity_rl_output_pb2___UnityRLOutputProto,
 )
 
 from typing import (
     DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
 
     @property
-    def rl_output(self) -> mlagents___envs___communicator_objects___unity_rl_output_pb2___UnityRLOutputProto: ...
+    def rl_output(self) -> mlagents_envs___communicator_objects___unity_rl_output_pb2___UnityRLOutputProto: ...
-    def rl_initialization_output(self) -> mlagents___envs___communicator_objects___unity_rl_initialization_output_pb2___UnityRLInitializationOutputProto: ...
+    def rl_initialization_output(self) -> mlagents_envs___communicator_objects___unity_rl_initialization_output_pb2___UnityRLInitializationOutputProto: ...
-        rl_output : typing___Optional[mlagents___envs___communicator_objects___unity_rl_output_pb2___UnityRLOutputProto] = None,
-        rl_initialization_output : typing___Optional[mlagents___envs___communicator_objects___unity_rl_initialization_output_pb2___UnityRLInitializationOutputProto] = None,
+        rl_output : typing___Optional[mlagents_envs___communicator_objects___unity_rl_output_pb2___UnityRLOutputProto] = None,
+        rl_initialization_output : typing___Optional[mlagents_envs___communicator_objects___unity_rl_initialization_output_pb2___UnityRLInitializationOutputProto] = None,
         ) -> None: ...
     @classmethod
     def FromString(cls, s: builtin___bytes) -> UnityOutputProto: ...

ml-agents-envs/mlagents_envs/communicator_objects/unity_output_pb2.py

 # Generated by the protocol buffer compiler.  DO NOT EDIT!
-# source: mlagents/envs/communicator_objects/unity_output.proto
+# source: mlagents_envs/communicator_objects/unity_output.proto
 
 import sys
 _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
 _sym_db = _symbol_database.Default()
 
 
-from mlagents.envs.communicator_objects import unity_rl_output_pb2 as mlagents_dot_envs_dot_communicator__objects_dot_unity__rl__output__pb2
-from mlagents.envs.communicator_objects import unity_rl_initialization_output_pb2 as mlagents_dot_envs_dot_communicator__objects_dot_unity__rl__initialization__output__pb2
+from mlagents_envs.communicator_objects import unity_rl_output_pb2 as mlagents__envs_dot_communicator__objects_dot_unity__rl__output__pb2
+from mlagents_envs.communicator_objects import unity_rl_initialization_output_pb2 as mlagents__envs_dot_communicator__objects_dot_unity__rl__initialization__output__pb2
-  name='mlagents/envs/communicator_objects/unity_output.proto',
+  name='mlagents_envs/communicator_objects/unity_output.proto',
-  serialized_pb=_b('\n5mlagents/envs/communicator_objects/unity_output.proto\x12\x14\x63ommunicator_objects\x1a\x38mlagents/envs/communicator_objects/unity_rl_output.proto\x1aGmlagents/envs/communicator_objects/unity_rl_initialization_output.proto\"\xa9\x01\n\x10UnityOutputProto\x12;\n\trl_output\x18\x01 \x01(\x0b\x32(.communicator_objects.UnityRLOutputProto\x12X\n\x18rl_initialization_output\x18\x02 \x01(\x0b\x32\x36.communicator_objects.UnityRLInitializationOutputProtoB\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
+  serialized_pb=_b('\n5mlagents_envs/communicator_objects/unity_output.proto\x12\x14\x63ommunicator_objects\x1a\x38mlagents_envs/communicator_objects/unity_rl_output.proto\x1aGmlagents_envs/communicator_objects/unity_rl_initialization_output.proto\"\xa9\x01\n\x10UnityOutputProto\x12;\n\trl_output\x18\x01 \x01(\x0b\x32(.communicator_objects.UnityRLOutputProto\x12X\n\x18rl_initialization_output\x18\x02 \x01(\x0b\x32\x36.communicator_objects.UnityRLInitializationOutputProtoB\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
-  dependencies=[mlagents_dot_envs_dot_communicator__objects_dot_unity__rl__output__pb2.DESCRIPTOR,mlagents_dot_envs_dot_communicator__objects_dot_unity__rl__initialization__output__pb2.DESCRIPTOR,])
+  dependencies=[mlagents__envs_dot_communicator__objects_dot_unity__rl__output__pb2.DESCRIPTOR,mlagents__envs_dot_communicator__objects_dot_unity__rl__initialization__output__pb2.DESCRIPTOR,])
 
 
 
   serialized_end=380,
 )
 
-_UNITYOUTPUTPROTO.fields_by_name['rl_output'].message_type = mlagents_dot_envs_dot_communicator__objects_dot_unity__rl__output__pb2._UNITYRLOUTPUTPROTO
-_UNITYOUTPUTPROTO.fields_by_name['rl_initialization_output'].message_type = mlagents_dot_envs_dot_communicator__objects_dot_unity__rl__initialization__output__pb2._UNITYRLINITIALIZATIONOUTPUTPROTO
+_UNITYOUTPUTPROTO.fields_by_name['rl_output'].message_type = mlagents__envs_dot_communicator__objects_dot_unity__rl__output__pb2._UNITYRLOUTPUTPROTO
+_UNITYOUTPUTPROTO.fields_by_name['rl_initialization_output'].message_type = mlagents__envs_dot_communicator__objects_dot_unity__rl__initialization__output__pb2._UNITYRLINITIALIZATIONOUTPUTPROTO
-  __module__ = 'mlagents.envs.communicator_objects.unity_output_pb2'
+  __module__ = 'mlagents_envs.communicator_objects.unity_output_pb2'
   # @@protoc_insertion_point(class_scope:communicator_objects.UnityOutputProto)
   ))
 _sym_db.RegisterMessage(UnityOutputProto)

ml-agents-envs/mlagents_envs/communicator_objects/unity_message_pb2.pyi

     Message as google___protobuf___message___Message,
 )
 
-from mlagents.envs.communicator_objects.header_pb2 import (
-    HeaderProto as mlagents___envs___communicator_objects___header_pb2___HeaderProto,
+from mlagents_envs.communicator_objects.header_pb2 import (
+    HeaderProto as mlagents_envs___communicator_objects___header_pb2___HeaderProto,
-from mlagents.envs.communicator_objects.unity_input_pb2 import (
-    UnityInputProto as mlagents___envs___communicator_objects___unity_input_pb2___UnityInputProto,
+from mlagents_envs.communicator_objects.unity_input_pb2 import (
+    UnityInputProto as mlagents_envs___communicator_objects___unity_input_pb2___UnityInputProto,
-from mlagents.envs.communicator_objects.unity_output_pb2 import (
-    UnityOutputProto as mlagents___envs___communicator_objects___unity_output_pb2___UnityOutputProto,
+from mlagents_envs.communicator_objects.unity_output_pb2 import (
+    UnityOutputProto as mlagents_envs___communicator_objects___unity_output_pb2___UnityOutputProto,
 )
 
 from typing import (
     DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
 
     @property
-    def header(self) -> mlagents___envs___communicator_objects___header_pb2___HeaderProto: ...
+    def header(self) -> mlagents_envs___communicator_objects___header_pb2___HeaderProto: ...
-    def unity_output(self) -> mlagents___envs___communicator_objects___unity_output_pb2___UnityOutputProto: ...
+    def unity_output(self) -> mlagents_envs___communicator_objects___unity_output_pb2___UnityOutputProto: ...
-    def unity_input(self) -> mlagents___envs___communicator_objects___unity_input_pb2___UnityInputProto: ...
+    def unity_input(self) -> mlagents_envs___communicator_objects___unity_input_pb2___UnityInputProto: ...
-        header : typing___Optional[mlagents___envs___communicator_objects___header_pb2___HeaderProto] = None,
-        unity_output : typing___Optional[mlagents___envs___communicator_objects___unity_output_pb2___UnityOutputProto] = None,
-        unity_input : typing___Optional[mlagents___envs___communicator_objects___unity_input_pb2___UnityInputProto] = None,
+        header : typing___Optional[mlagents_envs___communicator_objects___header_pb2___HeaderProto] = None,
+        unity_output : typing___Optional[mlagents_envs___communicator_objects___unity_output_pb2___UnityOutputProto] = None,
+        unity_input : typing___Optional[mlagents_envs___communicator_objects___unity_input_pb2___UnityInputProto] = None,
         ) -> None: ...
     @classmethod
     def FromString(cls, s: builtin___bytes) -> UnityMessageProto: ...

ml-agents-envs/mlagents_envs/communicator_objects/unity_message_pb2.py

 # Generated by the protocol buffer compiler.  DO NOT EDIT!
-# source: mlagents/envs/communicator_objects/unity_message.proto
+# source: mlagents_envs/communicator_objects/unity_message.proto
 
 import sys
 _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
 _sym_db = _symbol_database.Default()
 
 
-from mlagents.envs.communicator_objects import unity_output_pb2 as mlagents_dot_envs_dot_communicator__objects_dot_unity__output__pb2
-from mlagents.envs.communicator_objects import unity_input_pb2 as mlagents_dot_envs_dot_communicator__objects_dot_unity__input__pb2
-from mlagents.envs.communicator_objects import header_pb2 as mlagents_dot_envs_dot_communicator__objects_dot_header__pb2
+from mlagents_envs.communicator_objects import unity_output_pb2 as mlagents__envs_dot_communicator__objects_dot_unity__output__pb2
+from mlagents_envs.communicator_objects import unity_input_pb2 as mlagents__envs_dot_communicator__objects_dot_unity__input__pb2
+from mlagents_envs.communicator_objects import header_pb2 as mlagents__envs_dot_communicator__objects_dot_header__pb2
-  name='mlagents/envs/communicator_objects/unity_message.proto',
+  name='mlagents_envs/communicator_objects/unity_message.proto',
-  serialized_pb=_b('\n6mlagents/envs/communicator_objects/unity_message.proto\x12\x14\x63ommunicator_objects\x1a\x35mlagents/envs/communicator_objects/unity_output.proto\x1a\x34mlagents/envs/communicator_objects/unity_input.proto\x1a/mlagents/envs/communicator_objects/header.proto\"\xc0\x01\n\x11UnityMessageProto\x12\x31\n\x06header\x18\x01 \x01(\x0b\x32!.communicator_objects.HeaderProto\x12<\n\x0cunity_output\x18\x02 \x01(\x0b\x32&.communicator_objects.UnityOutputProto\x12:\n\x0bunity_input\x18\x03 \x01(\x0b\x32%.communicator_objects.UnityInputProtoB\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
+  serialized_pb=_b('\n6mlagents_envs/communicator_objects/unity_message.proto\x12\x14\x63ommunicator_objects\x1a\x35mlagents_envs/communicator_objects/unity_output.proto\x1a\x34mlagents_envs/communicator_objects/unity_input.proto\x1a/mlagents_envs/communicator_objects/header.proto\"\xc0\x01\n\x11UnityMessageProto\x12\x31\n\x06header\x18\x01 \x01(\x0b\x32!.communicator_objects.HeaderProto\x12<\n\x0cunity_output\x18\x02 \x01(\x0b\x32&.communicator_objects.UnityOutputProto\x12:\n\x0bunity_input\x18\x03 \x01(\x0b\x32%.communicator_objects.UnityInputProtoB\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
-  dependencies=[mlagents_dot_envs_dot_communicator__objects_dot_unity__output__pb2.DESCRIPTOR,mlagents_dot_envs_dot_communicator__objects_dot_unity__input__pb2.DESCRIPTOR,mlagents_dot_envs_dot_communicator__objects_dot_header__pb2.DESCRIPTOR,])
+  dependencies=[mlagents__envs_dot_communicator__objects_dot_unity__output__pb2.DESCRIPTOR,mlagents__envs_dot_communicator__objects_dot_unity__input__pb2.DESCRIPTOR,mlagents__envs_dot_communicator__objects_dot_header__pb2.DESCRIPTOR,])
 
 
 
   serialized_end=431,
 )
 
-_UNITYMESSAGEPROTO.fields_by_name['header'].message_type = mlagents_dot_envs_dot_communicator__objects_dot_header__pb2._HEADERPROTO
-_UNITYMESSAGEPROTO.fields_by_name['unity_output'].message_type = mlagents_dot_envs_dot_communicator__objects_dot_unity__output__pb2._UNITYOUTPUTPROTO
-_UNITYMESSAGEPROTO.fields_by_name['unity_input'].message_type = mlagents_dot_envs_dot_communicator__objects_dot_unity__input__pb2._UNITYINPUTPROTO
+_UNITYMESSAGEPROTO.fields_by_name['header'].message_type = mlagents__envs_dot_communicator__objects_dot_header__pb2._HEADERPROTO
+_UNITYMESSAGEPROTO.fields_by_name['unity_output'].message_type = mlagents__envs_dot_communicator__objects_dot_unity__output__pb2._UNITYOUTPUTPROTO
+_UNITYMESSAGEPROTO.fields_by_name['unity_input'].message_type = mlagents__envs_dot_communicator__objects_dot_unity__input__pb2._UNITYINPUTPROTO
-  __module__ = 'mlagents.envs.communicator_objects.unity_message_pb2'
+  __module__ = 'mlagents_envs.communicator_objects.unity_message_pb2'
   # @@protoc_insertion_point(class_scope:communicator_objects.UnityMessageProto)
   ))
 _sym_db.RegisterMessage(UnityMessageProto)

ml-agents-envs/mlagents_envs/communicator_objects/unity_input_pb2.pyi

     Message as google___protobuf___message___Message,
 )
 
-from mlagents.envs.communicator_objects.unity_rl_initialization_input_pb2 import (
-    UnityRLInitializationInputProto as mlagents___envs___communicator_objects___unity_rl_initialization_input_pb2___UnityRLInitializationInputProto,
+from mlagents_envs.communicator_objects.unity_rl_initialization_input_pb2 import (
+    UnityRLInitializationInputProto as mlagents_envs___communicator_objects___unity_rl_initialization_input_pb2___UnityRLInitializationInputProto,
-from mlagents.envs.communicator_objects.unity_rl_input_pb2 import (
-    UnityRLInputProto as mlagents___envs___communicator_objects___unity_rl_input_pb2___UnityRLInputProto,
+from mlagents_envs.communicator_objects.unity_rl_input_pb2 import (
+    UnityRLInputProto as mlagents_envs___communicator_objects___unity_rl_input_pb2___UnityRLInputProto,
 )
 
 from typing import (
     DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
 
     @property
-    def rl_input(self) -> mlagents___envs___communicator_objects___unity_rl_input_pb2___UnityRLInputProto: ...
+    def rl_input(self) -> mlagents_envs___communicator_objects___unity_rl_input_pb2___UnityRLInputProto: ...
-    def rl_initialization_input(self) -> mlagents___envs___communicator_objects___unity_rl_initialization_input_pb2___UnityRLInitializationInputProto: ...
+    def rl_initialization_input(self) -> mlagents_envs___communicator_objects___unity_rl_initialization_input_pb2___UnityRLInitializationInputProto: ...
-        rl_input : typing___Optional[mlagents___envs___communicator_objects___unity_rl_input_pb2___UnityRLInputProto] = None,
-        rl_initialization_input : typing___Optional[mlagents___envs___communicator_objects___unity_rl_initialization_input_pb2___UnityRLInitializationInputProto] = None,
+        rl_input : typing___Optional[mlagents_envs___communicator_objects___unity_rl_input_pb2___UnityRLInputProto] = None,
+        rl_initialization_input : typing___Optional[mlagents_envs___communicator_objects___unity_rl_initialization_input_pb2___UnityRLInitializationInputProto] = None,
         ) -> None: ...
     @classmethod
     def FromString(cls, s: builtin___bytes) -> UnityInputProto: ...

ml-agents-envs/mlagents_envs/communicator_objects/unity_input_pb2.py

 # Generated by the protocol buffer compiler.  DO NOT EDIT!
-# source: mlagents/envs/communicator_objects/unity_input.proto
+# source: mlagents_envs/communicator_objects/unity_input.proto
 
 import sys
 _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
 _sym_db = _symbol_database.Default()
 
 
-from mlagents.envs.communicator_objects import unity_rl_input_pb2 as mlagents_dot_envs_dot_communicator__objects_dot_unity__rl__input__pb2
-from mlagents.envs.communicator_objects import unity_rl_initialization_input_pb2 as mlagents_dot_envs_dot_communicator__objects_dot_unity__rl__initialization__input__pb2
+from mlagents_envs.communicator_objects import unity_rl_input_pb2 as mlagents__envs_dot_communicator__objects_dot_unity__rl__input__pb2
+from mlagents_envs.communicator_objects import unity_rl_initialization_input_pb2 as mlagents__envs_dot_communicator__objects_dot_unity__rl__initialization__input__pb2
-  name='mlagents/envs/communicator_objects/unity_input.proto',
+  name='mlagents_envs/communicator_objects/unity_input.proto',
-  serialized_pb=_b('\n4mlagents/envs/communicator_objects/unity_input.proto\x12\x14\x63ommunicator_objects\x1a\x37mlagents/envs/communicator_objects/unity_rl_input.proto\x1a\x46mlagents/envs/communicator_objects/unity_rl_initialization_input.proto\"\xa4\x01\n\x0fUnityInputProto\x12\x39\n\x08rl_input\x18\x01 \x01(\x0b\x32\'.communicator_objects.UnityRLInputProto\x12V\n\x17rl_initialization_input\x18\x02 \x01(\x0b\x32\x35.communicator_objects.UnityRLInitializationInputProtoB\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
+  serialized_pb=_b('\n4mlagents_envs/communicator_objects/unity_input.proto\x12\x14\x63ommunicator_objects\x1a\x37mlagents_envs/communicator_objects/unity_rl_input.proto\x1a\x46mlagents_envs/communicator_objects/unity_rl_initialization_input.proto\"\xa4\x01\n\x0fUnityInputProto\x12\x39\n\x08rl_input\x18\x01 \x01(\x0b\x32\'.communicator_objects.UnityRLInputProto\x12V\n\x17rl_initialization_input\x18\x02 \x01(\x0b\x32\x35.communicator_objects.UnityRLInitializationInputProtoB\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
-  dependencies=[mlagents_dot_envs_dot_communicator__objects_dot_unity__rl__input__pb2.DESCRIPTOR,mlagents_dot_envs_dot_communicator__objects_dot_unity__rl__initialization__input__pb2.DESCRIPTOR,])
+  dependencies=[mlagents__envs_dot_communicator__objects_dot_unity__rl__input__pb2.DESCRIPTOR,mlagents__envs_dot_communicator__objects_dot_unity__rl__initialization__input__pb2.DESCRIPTOR,])
 
 
 
   serialized_end=372,
 )
 
-_UNITYINPUTPROTO.fields_by_name['rl_input'].message_type = mlagents_dot_envs_dot_communicator__objects_dot_unity__rl__input__pb2._UNITYRLINPUTPROTO
-_UNITYINPUTPROTO.fields_by_name['rl_initialization_input'].message_type = mlagents_dot_envs_dot_communicator__objects_dot_unity__rl__initialization__input__pb2._UNITYRLINITIALIZATIONINPUTPROTO
+_UNITYINPUTPROTO.fields_by_name['rl_input'].message_type = mlagents__envs_dot_communicator__objects_dot_unity__rl__input__pb2._UNITYRLINPUTPROTO
+_UNITYINPUTPROTO.fields_by_name['rl_initialization_input'].message_type = mlagents__envs_dot_communicator__objects_dot_unity__rl__initialization__input__pb2._UNITYRLINITIALIZATIONINPUTPROTO
-  __module__ = 'mlagents.envs.communicator_objects.unity_input_pb2'
+  __module__ = 'mlagents_envs.communicator_objects.unity_input_pb2'
   # @@protoc_insertion_point(class_scope:communicator_objects.UnityInputProto)
   ))
 _sym_db.RegisterMessage(UnityInputProto)

ml-agents-envs/mlagents_envs/communicator_objects/space_type_pb2.py

 # Generated by the protocol buffer compiler.  DO NOT EDIT!
-# source: mlagents/envs/communicator_objects/space_type.proto
+# source: mlagents_envs/communicator_objects/space_type.proto
 
 import sys
 _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
 
 
 DESCRIPTOR = _descriptor.FileDescriptor(
-  name='mlagents/envs/communicator_objects/space_type.proto',
+  name='mlagents_envs/communicator_objects/space_type.proto',
-  serialized_pb=_b('\n3mlagents/envs/communicator_objects/space_type.proto\x12\x14\x63ommunicator_objects*.\n\x0eSpaceTypeProto\x12\x0c\n\x08\x64iscrete\x10\x00\x12\x0e\n\ncontinuous\x10\x01\x42\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
+  serialized_pb=_b('\n3mlagents_envs/communicator_objects/space_type.proto\x12\x14\x63ommunicator_objects*.\n\x0eSpaceTypeProto\x12\x0c\n\x08\x64iscrete\x10\x00\x12\x0e\n\ncontinuous\x10\x01\x42\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
 )
 
 _SPACETYPEPROTO = _descriptor.EnumDescriptor(

ml-agents-envs/mlagents_envs/communicator_objects/observation_pb2.py

 # Generated by the protocol buffer compiler.  DO NOT EDIT!
-# source: mlagents/envs/communicator_objects/observation.proto
+# source: mlagents_envs/communicator_objects/observation.proto
 
 import sys
 _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
 
 
 DESCRIPTOR = _descriptor.FileDescriptor(
-  name='mlagents/envs/communicator_objects/observation.proto',
+  name='mlagents_envs/communicator_objects/observation.proto',
-  serialized_pb=_b('\n4mlagents/envs/communicator_objects/observation.proto\x12\x14\x63ommunicator_objects\"\xf9\x01\n\x10ObservationProto\x12\r\n\x05shape\x18\x01 \x03(\x05\x12\x44\n\x10\x63ompression_type\x18\x02 \x01(\x0e\x32*.communicator_objects.CompressionTypeProto\x12\x19\n\x0f\x63ompressed_data\x18\x03 \x01(\x0cH\x00\x12\x46\n\nfloat_data\x18\x04 \x01(\x0b\x32\x30.communicator_objects.ObservationProto.FloatDataH\x00\x1a\x19\n\tFloatData\x12\x0c\n\x04\x64\x61ta\x18\x01 \x03(\x02\x42\x12\n\x10observation_data*)\n\x14\x43ompressionTypeProto\x12\x08\n\x04NONE\x10\x00\x12\x07\n\x03PNG\x10\x01\x42\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
+  serialized_pb=_b('\n4mlagents_envs/communicator_objects/observation.proto\x12\x14\x63ommunicator_objects\"\xf9\x01\n\x10ObservationProto\x12\r\n\x05shape\x18\x01 \x03(\x05\x12\x44\n\x10\x63ompression_type\x18\x02 \x01(\x0e\x32*.communicator_objects.CompressionTypeProto\x12\x19\n\x0f\x63ompressed_data\x18\x03 \x01(\x0cH\x00\x12\x46\n\nfloat_data\x18\x04 \x01(\x0b\x32\x30.communicator_objects.ObservationProto.FloatDataH\x00\x1a\x19\n\tFloatData\x12\x0c\n\x04\x64\x61ta\x18\x01 \x03(\x02\x42\x12\n\x10observation_data*)\n\x14\x43ompressionTypeProto\x12\x08\n\x04NONE\x10\x00\x12\x07\n\x03PNG\x10\x01\x42\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
 )
 
 _COMPRESSIONTYPEPROTO = _descriptor.EnumDescriptor(
 
   FloatData = _reflection.GeneratedProtocolMessageType('FloatData', (_message.Message,), dict(
     DESCRIPTOR = _OBSERVATIONPROTO_FLOATDATA,
-    __module__ = 'mlagents.envs.communicator_objects.observation_pb2'
+    __module__ = 'mlagents_envs.communicator_objects.observation_pb2'
-  __module__ = 'mlagents.envs.communicator_objects.observation_pb2'
+  __module__ = 'mlagents_envs.communicator_objects.observation_pb2'
   # @@protoc_insertion_point(class_scope:communicator_objects.ObservationProto)
   ))
 _sym_db.RegisterMessage(ObservationProto)

ml-agents-envs/mlagents_envs/communicator_objects/header_pb2.py

 # Generated by the protocol buffer compiler.  DO NOT EDIT!
-# source: mlagents/envs/communicator_objects/header.proto
+# source: mlagents_envs/communicator_objects/header.proto
 
 import sys
 _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
 
 
 DESCRIPTOR = _descriptor.FileDescriptor(
-  name='mlagents/envs/communicator_objects/header.proto',
+  name='mlagents_envs/communicator_objects/header.proto',
-  serialized_pb=_b('\n/mlagents/envs/communicator_objects/header.proto\x12\x14\x63ommunicator_objects\".\n\x0bHeaderProto\x12\x0e\n\x06status\x18\x01 \x01(\x05\x12\x0f\n\x07message\x18\x02 \x01(\tB\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
+  serialized_pb=_b('\n/mlagents_envs/communicator_objects/header.proto\x12\x14\x63ommunicator_objects\".\n\x0bHeaderProto\x12\x0e\n\x06status\x18\x01 \x01(\x05\x12\x0f\n\x07message\x18\x02 \x01(\tB\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
 )
 
 
 
 HeaderProto = _reflection.GeneratedProtocolMessageType('HeaderProto', (_message.Message,), dict(
   DESCRIPTOR = _HEADERPROTO,
-  __module__ = 'mlagents.envs.communicator_objects.header_pb2'
+  __module__ = 'mlagents_envs.communicator_objects.header_pb2'
   # @@protoc_insertion_point(class_scope:communicator_objects.HeaderProto)
   ))
 _sym_db.RegisterMessage(HeaderProto)