Remove {text,custom} {action,observations} (#2839)

* delete text actions and obs

* delete custom actions and obs

* regenerate protos

* cleanup C#

* format

* fix tests

* fix base env signature

* doc cleanup

UnitySDK/Assets/ML-Agents/Editor/Tests/DemonstrationTests.cs

                 id = 5,
                 maxStepReached = true,
                 floatObservations = new List<float>() { 1f, 1f, 1f },
-                storedTextActions = "TestAction",
-                textObservation = "TestAction",
             };
 
             demoStore.Record(agentInfo);

UnitySDK/Assets/ML-Agents/Editor/Tests/MLAgentsEditModeTest.cs

             AddVectorObs(0f);
         }
 
-        public override void AgentAction(float[] vectorAction, string textAction)
+        public override void AgentAction(float[] vectorAction)
         {
             agentActionCalls += 1;
             AddReward(0.1f);

UnitySDK/Assets/ML-Agents/Examples/3DBall/Scripts/Ball3DAgent.cs

         AddVectorObs(m_BallRb.velocity);
     }
 
-    public override void AgentAction(float[] vectorAction, string textAction)
+    public override void AgentAction(float[] vectorAction)
     {
         var actionZ = 2f * Mathf.Clamp(vectorAction[0], -1f, 1f);
         var actionX = 2f * Mathf.Clamp(vectorAction[1], -1f, 1f);

UnitySDK/Assets/ML-Agents/Examples/3DBall/Scripts/Ball3DHardAgent.cs

         AddVectorObs((ball.transform.position - gameObject.transform.position));
     }
 
-    public override void AgentAction(float[] vectorAction, string textAction)
+    public override void AgentAction(float[] vectorAction)
     {
         var actionZ = 2f * Mathf.Clamp(vectorAction[0], -1f, 1f);
         var actionX = 2f * Mathf.Clamp(vectorAction[1], -1f, 1f);

UnitySDK/Assets/ML-Agents/Examples/Basic/Scripts/BasicAgent.cs

         AddVectorObs(m_Position, 20);
     }
 
-    public override void AgentAction(float[] vectorAction, string textAction)
+    public override void AgentAction(float[] vectorAction)
     {
         var movement = (int)vectorAction[0];
 

UnitySDK/Assets/ML-Agents/Examples/Bouncer/Scripts/BouncerAgent.cs

         AddVectorObs(target.transform.localPosition);
     }
 
-    public override void AgentAction(float[] vectorAction, string textAction)
+    public override void AgentAction(float[] vectorAction)
     {
         for (var i = 0; i < vectorAction.Length; i++)
         {

UnitySDK/Assets/ML-Agents/Examples/Crawler/Scripts/CrawlerAgent.cs

         target.position = newTargetPos + ground.position;
     }
 
-    public override void AgentAction(float[] vectorAction, string textAction)
+    public override void AgentAction(float[] vectorAction)
     {
         if (detectTargets)
         {

UnitySDK/Assets/ML-Agents/Examples/FoodCollector/Scripts/FoodCollectorAgent.cs

         gameObject.GetComponentInChildren<Renderer>().material = normalMaterial;
     }
 
-    public override void AgentAction(float[] vectorAction, string textAction)
+    public override void AgentAction(float[] vectorAction)
     {
         MoveAgent(vectorAction);
     }

UnitySDK/Assets/ML-Agents/Examples/GridWorld/Scripts/GridAgent.cs

     }
 
     // to be implemented by the developer
-    public override void AgentAction(float[] vectorAction, string textAction)
+    public override void AgentAction(float[] vectorAction)
     {
         AddReward(-0.01f);
         var action = Mathf.FloorToInt(vectorAction[0]);

UnitySDK/Assets/ML-Agents/Examples/Hallway/Scripts/HallwayAgent.cs

         m_AgentRb.AddForce(dirToGo * m_Academy.agentRunSpeed, ForceMode.VelocityChange);
     }
 
-    public override void AgentAction(float[] vectorAction, string textAction)
+    public override void AgentAction(float[] vectorAction)
     {
         AddReward(-1f / agentParameters.maxStep);
         MoveAgent(vectorAction);

UnitySDK/Assets/ML-Agents/Examples/PushBlock/Scripts/PushAgentBasic.cs

     /// <summary>
     /// Called every step of the engine. Here the agent takes an action.
     /// </summary>
-    public override void AgentAction(float[] vectorAction, string textAction)
+    public override void AgentAction(float[] vectorAction)
     {
         // Move the agent using the action.
         MoveAgent(vectorAction);

UnitySDK/Assets/ML-Agents/Examples/Pyramids/Scripts/PyramidAgent.cs

         m_AgentRb.AddForce(dirToGo * 2f, ForceMode.VelocityChange);
     }
 
-    public override void AgentAction(float[] vectorAction, string textAction)
+    public override void AgentAction(float[] vectorAction)
     {
         AddReward(-1f / agentParameters.maxStep);
         MoveAgent(vectorAction);

UnitySDK/Assets/ML-Agents/Examples/Reacher/Scripts/ReacherAgent.cs

     /// <summary>
     /// The agent's four actions correspond to torques on each of the two joints.
     /// </summary>
-    public override void AgentAction(float[] vectorAction, string textAction)
+    public override void AgentAction(float[] vectorAction)
     {
         m_GoalDegree += m_GoalSpeed;
         UpdateGoalPosition();

UnitySDK/Assets/ML-Agents/Examples/Soccer/Scripts/AgentSoccer.cs

             ForceMode.VelocityChange);
     }
 
-    public override void AgentAction(float[] vectorAction, string textAction)
+    public override void AgentAction(float[] vectorAction)
     {
         // Existential penalty for strikers.
         if (agentRole == AgentRole.Striker)

UnitySDK/Assets/ML-Agents/Examples/Template/Scripts/TemplateAgent.cs

     {
     }
 
-    public override void AgentAction(float[] vectorAction, string textAction)
+    public override void AgentAction(float[] vectorAction)
     {
     }
 

UnitySDK/Assets/ML-Agents/Examples/Tennis/Scripts/TennisAgent.cs

         AddVectorObs(m_BallRb.velocity.y);
     }
 
-    public override void AgentAction(float[] vectorAction, string textAction)
+    public override void AgentAction(float[] vectorAction)
     {
         var moveX = Mathf.Clamp(vectorAction[0], -1f, 1f) * m_InvertMult;
         var moveY = Mathf.Clamp(vectorAction[1], -1f, 1f);

UnitySDK/Assets/ML-Agents/Examples/Walker/Scripts/WalkerAgent.cs

         }
     }
 
-    public override void AgentAction(float[] vectorAction, string textAction)
+    public override void AgentAction(float[] vectorAction)
     {
         m_DirToTarget = target.position - m_JdController.bodyPartsDict[hips].rb.position;
 

UnitySDK/Assets/ML-Agents/Examples/WallJump/Scripts/WallJumpAgent.cs

         jumpingTime -= Time.fixedDeltaTime;
     }
 
-    public override void AgentAction(float[] vectorAction, string textAction)
+    public override void AgentAction(float[] vectorAction)
     {
         MoveAgent(vectorAction);
         if ((!Physics.Raycast(m_AgentRb.position, Vector3.down, 20))

UnitySDK/Assets/ML-Agents/Scripts/Agent.cs

         public List<float> floatObservations;
 
         /// <summary>
-        /// Most recent text observation.
-        /// </summary>
-        public string textObservation;
-
-        /// <summary>
-
-        /// <summary>
-        /// Keeps track of the last text action taken by the Brain.
-        /// </summary>
-        public string storedTextActions;
 
         /// <summary>
         /// For discrete control, specifies the actions that the agent cannot take. Is true if
         /// to separate between different agents in the environment.
         /// </summary>
         public int id;
-
-        /// <summary>
-        /// User-customizable object for sending structured output from Unity to Python in response
-        /// to an action in addition to a scalar reward.
-        /// TODO(cgoy): All references to protobuf objects should be removed.
-        /// </summary>
-        public CommunicatorObjects.CustomObservationProto customObservation;
     }
 
     /// <summary>
     public struct AgentAction
     {
         public float[] vectorActions;
-        public string textActions;
-        /// TODO(cgoy): All references to protobuf objects should be removed.
-        public CommunicatorObjects.CustomActionProto customAction;
     }
 
     /// <summary>
                 }
             }
 
-            if (m_Info.textObservation == null)
-                m_Info.textObservation = "";
-            m_Action.textActions = "";
-
-            m_Info.customObservation = null;
         }
 
         /// <summary>
             }
 
             m_Info.storedVectorActions = m_Action.vectorActions;
-            m_Info.storedTextActions = m_Action.textActions;
             m_Info.compressedObservations.Clear();
             m_ActionMasker.ResetMask();
             using (TimerStack.Instance.Scoped("CollectObservations"))
                 m_Recorder.WriteExperience(m_Info);
             }
 
-            m_Info.textObservation = "";
         }
 
         /// <summary>
         }
 
         /// <summary>
-        /// Collects the (vector, visual, text) observations of the agent.
+        /// Collects the (vector, visual) observations of the agent.
         /// The agent observation describes the current environment from the
         /// perspective of the agent.
         /// </summary>
         /// observation could include distances to friends or enemies, or the
         /// current level of ammunition at its disposal.
-        /// Recall that an Agent may attach vector, visual or textual observations.
+        /// Recall that an Agent may attach vector or visual observations.
         /// Vector observations are added by calling the provided helper methods:
         ///     - <see cref="AddVectorObs(int)"/>
         ///     - <see cref="AddVectorObs(float)"/>
         /// needs to match the vectorObservationSize attribute of the linked Brain.
         /// Visual observations are implicitly added from the cameras attached to
         /// the Agent.
-        /// Lastly, textual observations are added using
-        /// <see cref="SetTextObs(string)"/>.
         /// </remarks>
         public virtual void CollectObservations()
         {
         }
 
         /// <summary>
-        /// Sets the text observation.
-        /// </summary>
-        /// <param name="textObservation">The text observation.</param>
-        public void SetTextObs(string textObservation)
-        {
-            m_Info.textObservation = textObservation;
-        }
-
-        /// <summary>
         /// Specifies the agent behavior at every step based on the provided
         /// action.
         /// </summary>
         /// </param>
-        /// <param name="textAction">Text action.</param>
-        public virtual void AgentAction(float[] vectorAction, string textAction)
-        {
-        }
-
-        /// <summary>
-        /// Specifies the agent behavior at every step based on the provided
-        /// action.
-        /// </summary>
-        /// <param name="vectorAction">
-        /// Vector action. Note that for discrete actions, the provided array
-        /// will be of length 1.
-        /// </param>
-        /// <param name="textAction">Text action.</param>
-        /// <param name="customAction">
-        /// A custom action, defined by the user as custom protobuf message. Useful if the action is hard to encode
-        /// as either a flat vector or a single string.
-        /// </param>
-        public virtual void AgentAction(float[] vectorAction, string textAction, CommunicatorObjects.CustomActionProto customAction)
+        public virtual void AgentAction(float[] vectorAction)
-            // We fall back to not using the custom action if the subclassed Agent doesn't override this method.
-            AgentAction(vectorAction, textAction);
         }
 
         /// <summary>
             if ((m_RequestAction) && (m_Brain != null))
             {
                 m_RequestAction = false;
-                AgentAction(m_Action.vectorActions, m_Action.textActions, m_Action.customAction);
+                AgentAction(m_Action.vectorActions);
             }
 
             if ((m_StepCount >= agentParameters.maxStep)
         void DecideAction()
         {
             m_Brain?.DecideAction();
-        }
-
-        /// <summary>
-        /// Sets the custom observation for the agent for this episode.
-        /// </summary>
-        /// <param name="customObservation">New value of the agent's custom observation.</param>
-        public void SetCustomObservation(CommunicatorObjects.CustomObservationProto customObservation)
-        {
-            m_Info.customObservation = customObservation;
         }
     }
 }

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

       byte[] descriptorData = global::System.Convert.FromBase64String(
           string.Concat(
             "CjVtbGFnZW50cy9lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL2FnZW50X2Fj",
-            "dGlvbi5wcm90bxIUY29tbXVuaWNhdG9yX29iamVjdHMaNm1sYWdlbnRzL2Vu",
-            "dnMvY29tbXVuaWNhdG9yX29iamVjdHMvY3VzdG9tX2FjdGlvbi5wcm90byKV",
-            "AQoQQWdlbnRBY3Rpb25Qcm90bxIWCg52ZWN0b3JfYWN0aW9ucxgBIAMoAhIU",
-            "Cgx0ZXh0X2FjdGlvbnMYAiABKAkSDQoFdmFsdWUYBCABKAISPgoNY3VzdG9t",
-            "X2FjdGlvbhgFIAEoCzInLmNvbW11bmljYXRvcl9vYmplY3RzLkN1c3RvbUFj",
-            "dGlvblByb3RvSgQIAxAEQh+qAhxNTEFnZW50cy5Db21tdW5pY2F0b3JPYmpl",
-            "Y3RzYgZwcm90bzM="));
+            "dGlvbi5wcm90bxIUY29tbXVuaWNhdG9yX29iamVjdHMiSwoQQWdlbnRBY3Rp",
+            "b25Qcm90bxIWCg52ZWN0b3JfYWN0aW9ucxgBIAMoAhINCgV2YWx1ZRgEIAEo",
+            "AkoECAIQA0oECAMQBEoECAUQBkIfqgIcTUxBZ2VudHMuQ29tbXVuaWNhdG9y",
+            "T2JqZWN0c2IGcHJvdG8z"));
-          new pbr::FileDescriptor[] { global::MLAgents.CommunicatorObjects.CustomActionReflection.Descriptor, },
+          new pbr::FileDescriptor[] { },
-            new pbr::GeneratedClrTypeInfo(typeof(global::MLAgents.CommunicatorObjects.AgentActionProto), global::MLAgents.CommunicatorObjects.AgentActionProto.Parser, new[]{ "VectorActions", "TextActions", "Value", "CustomAction" }, null, null, null)
+            new pbr::GeneratedClrTypeInfo(typeof(global::MLAgents.CommunicatorObjects.AgentActionProto), global::MLAgents.CommunicatorObjects.AgentActionProto.Parser, new[]{ "VectorActions", "Value" }, null, null, null)
           }));
     }
     #endregion
     [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
     public AgentActionProto(AgentActionProto other) : this() {
       vectorActions_ = other.vectorActions_.Clone();
-      textActions_ = other.textActions_;
-      CustomAction = other.customAction_ != null ? other.CustomAction.Clone() : null;
       _unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields);
     }
 
       get { return vectorActions_; }
     }
 
-    /// <summary>Field number for the "text_actions" field.</summary>
-    public const int TextActionsFieldNumber = 2;
-    private string textActions_ = "";
-    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
-    public string TextActions {
-      get { return textActions_; }
-      set {
-        textActions_ = pb::ProtoPreconditions.CheckNotNull(value, "value");
-      }
-    }
-
     /// <summary>Field number for the "value" field.</summary>
     public const int ValueFieldNumber = 4;
     private float value_;
       set {
         value_ = value;
-      }
-    }
-
-    /// <summary>Field number for the "custom_action" field.</summary>
-    public const int CustomActionFieldNumber = 5;
-    private global::MLAgents.CommunicatorObjects.CustomActionProto customAction_;
-    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
-    public global::MLAgents.CommunicatorObjects.CustomActionProto CustomAction {
-      get { return customAction_; }
-      set {
-        customAction_ = value;
       }
     }
 
         return true;
       }
       if(!vectorActions_.Equals(other.vectorActions_)) return false;
-      if (TextActions != other.TextActions) return false;
-      if (!object.Equals(CustomAction, other.CustomAction)) return false;
       return Equals(_unknownFields, other._unknownFields);
     }
 
       hash ^= vectorActions_.GetHashCode();
-      if (TextActions.Length != 0) hash ^= TextActions.GetHashCode();
-      if (customAction_ != null) hash ^= CustomAction.GetHashCode();
       if (_unknownFields != null) {
         hash ^= _unknownFields.GetHashCode();
       }
     [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
     public void WriteTo(pb::CodedOutputStream output) {
       vectorActions_.WriteTo(output, _repeated_vectorActions_codec);
-      if (TextActions.Length != 0) {
-        output.WriteRawTag(18);
-        output.WriteString(TextActions);
-      }
-      if (customAction_ != null) {
-        output.WriteRawTag(42);
-        output.WriteMessage(CustomAction);
-      }
       if (_unknownFields != null) {
         _unknownFields.WriteTo(output);
       }
     public int CalculateSize() {
       int size = 0;
       size += vectorActions_.CalculateSize(_repeated_vectorActions_codec);
-      if (TextActions.Length != 0) {
-        size += 1 + pb::CodedOutputStream.ComputeStringSize(TextActions);
-      }
-      if (customAction_ != null) {
-        size += 1 + pb::CodedOutputStream.ComputeMessageSize(CustomAction);
-      }
       if (_unknownFields != null) {
         size += _unknownFields.CalculateSize();
       }
         return;
       }
       vectorActions_.Add(other.vectorActions_);
-      if (other.TextActions.Length != 0) {
-        TextActions = other.TextActions;
-      }
-      }
-      if (other.customAction_ != null) {
-        if (customAction_ == null) {
-          customAction_ = new global::MLAgents.CommunicatorObjects.CustomActionProto();
-        }
-        CustomAction.MergeFrom(other.CustomAction);
       }
       _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields);
     }
             vectorActions_.AddEntriesFrom(input, _repeated_vectorActions_codec);
             break;
           }
-          case 18: {
-            TextActions = input.ReadString();
-            break;
-          }
-            break;
-          }
-          case 42: {
-            if (customAction_ == null) {
-              customAction_ = new global::MLAgents.CommunicatorObjects.CustomActionProto();
-            }
-            input.ReadMessage(customAction_);
             break;
           }
         }

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

             "CjNtbGFnZW50cy9lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL2FnZW50X2lu",
             "Zm8ucHJvdG8SFGNvbW11bmljYXRvcl9vYmplY3RzGj9tbGFnZW50cy9lbnZz",
             "L2NvbW11bmljYXRvcl9vYmplY3RzL2NvbXByZXNzZWRfb2JzZXJ2YXRpb24u",
-            "cHJvdG8aO21sYWdlbnRzL2VudnMvY29tbXVuaWNhdG9yX29iamVjdHMvY3Vz",
-            "dG9tX29ic2VydmF0aW9uLnByb3RvIowDCg5BZ2VudEluZm9Qcm90bxIiChpz",
-            "dGFja2VkX3ZlY3Rvcl9vYnNlcnZhdGlvbhgBIAMoAhIYChB0ZXh0X29ic2Vy",
-            "dmF0aW9uGAMgASgJEh0KFXN0b3JlZF92ZWN0b3JfYWN0aW9ucxgEIAMoAhIb",
-            "ChNzdG9yZWRfdGV4dF9hY3Rpb25zGAUgASgJEg4KBnJld2FyZBgHIAEoAhIM",
-            "CgRkb25lGAggASgIEhgKEG1heF9zdGVwX3JlYWNoZWQYCSABKAgSCgoCaWQY",
-            "CiABKAUSEwoLYWN0aW9uX21hc2sYCyADKAgSSAoSY3VzdG9tX29ic2VydmF0",
-            "aW9uGAwgASgLMiwuY29tbXVuaWNhdG9yX29iamVjdHMuQ3VzdG9tT2JzZXJ2",
-            "YXRpb25Qcm90bxJRChdjb21wcmVzc2VkX29ic2VydmF0aW9ucxgNIAMoCzIw",
-            "LmNvbW11bmljYXRvcl9vYmplY3RzLkNvbXByZXNzZWRPYnNlcnZhdGlvblBy",
-            "b3RvSgQIAhADSgQIBhAHQh+qAhxNTEFnZW50cy5Db21tdW5pY2F0b3JPYmpl",
-            "Y3RzYgZwcm90bzM="));
+            "cHJvdG8inQIKDkFnZW50SW5mb1Byb3RvEiIKGnN0YWNrZWRfdmVjdG9yX29i",
+            "c2VydmF0aW9uGAEgAygCEh0KFXN0b3JlZF92ZWN0b3JfYWN0aW9ucxgEIAMo",
+            "AhIOCgZyZXdhcmQYByABKAISDAoEZG9uZRgIIAEoCBIYChBtYXhfc3RlcF9y",
+            "ZWFjaGVkGAkgASgIEgoKAmlkGAogASgFEhMKC2FjdGlvbl9tYXNrGAsgAygI",
+            "ElEKF2NvbXByZXNzZWRfb2JzZXJ2YXRpb25zGA0gAygLMjAuY29tbXVuaWNh",
+            "dG9yX29iamVjdHMuQ29tcHJlc3NlZE9ic2VydmF0aW9uUHJvdG9KBAgCEANK",
+            "BAgDEARKBAgFEAZKBAgGEAdKBAgMEA1CH6oCHE1MQWdlbnRzLkNvbW11bmlj",
+            "YXRvck9iamVjdHNiBnByb3RvMw=="));
-          new pbr::FileDescriptor[] { global::MLAgents.CommunicatorObjects.CompressedObservationReflection.Descriptor, global::MLAgents.CommunicatorObjects.CustomObservationReflection.Descriptor, },
+          new pbr::FileDescriptor[] { global::MLAgents.CommunicatorObjects.CompressedObservationReflection.Descriptor, },
-            new pbr::GeneratedClrTypeInfo(typeof(global::MLAgents.CommunicatorObjects.AgentInfoProto), global::MLAgents.CommunicatorObjects.AgentInfoProto.Parser, new[]{ "StackedVectorObservation", "TextObservation", "StoredVectorActions", "StoredTextActions", "Reward", "Done", "MaxStepReached", "Id", "ActionMask", "CustomObservation", "CompressedObservations" }, null, null, null)
+            new pbr::GeneratedClrTypeInfo(typeof(global::MLAgents.CommunicatorObjects.AgentInfoProto), global::MLAgents.CommunicatorObjects.AgentInfoProto.Parser, new[]{ "StackedVectorObservation", "StoredVectorActions", "Reward", "Done", "MaxStepReached", "Id", "ActionMask", "CompressedObservations" }, null, null, null)
           }));
     }
     #endregion
     [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
     public AgentInfoProto(AgentInfoProto other) : this() {
       stackedVectorObservation_ = other.stackedVectorObservation_.Clone();
-      textObservation_ = other.textObservation_;
-      storedTextActions_ = other.storedTextActions_;
-      CustomObservation = other.customObservation_ != null ? other.CustomObservation.Clone() : null;
       compressedObservations_ = other.compressedObservations_.Clone();
       _unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields);
     }
       get { return stackedVectorObservation_; }
     }
 
-    /// <summary>Field number for the "text_observation" field.</summary>
-    public const int TextObservationFieldNumber = 3;
-    private string textObservation_ = "";
-    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
-    public string TextObservation {
-      get { return textObservation_; }
-      set {
-        textObservation_ = pb::ProtoPreconditions.CheckNotNull(value, "value");
-      }
-    }
-
     /// <summary>Field number for the "stored_vector_actions" field.</summary>
     public const int StoredVectorActionsFieldNumber = 4;
     private static readonly pb::FieldCodec<float> _repeated_storedVectorActions_codec
     public pbc::RepeatedField<float> StoredVectorActions {
       get { return storedVectorActions_; }
-    }
-
-    /// <summary>Field number for the "stored_text_actions" field.</summary>
-    public const int StoredTextActionsFieldNumber = 5;
-    private string storedTextActions_ = "";
-    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
-    public string StoredTextActions {
-      get { return storedTextActions_; }
-      set {
-        storedTextActions_ = pb::ProtoPreconditions.CheckNotNull(value, "value");
-      }
     }
 
     /// <summary>Field number for the "reward" field.</summary>
       get { return actionMask_; }
     }
 
-    /// <summary>Field number for the "custom_observation" field.</summary>
-    public const int CustomObservationFieldNumber = 12;
-    private global::MLAgents.CommunicatorObjects.CustomObservationProto customObservation_;
-    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
-    public global::MLAgents.CommunicatorObjects.CustomObservationProto CustomObservation {
-      get { return customObservation_; }
-      set {
-        customObservation_ = value;
-      }
-    }
-
     /// <summary>Field number for the "compressed_observations" field.</summary>
     public const int CompressedObservationsFieldNumber = 13;
     private static readonly pb::FieldCodec<global::MLAgents.CommunicatorObjects.CompressedObservationProto> _repeated_compressedObservations_codec
         return true;
       }
       if(!stackedVectorObservation_.Equals(other.stackedVectorObservation_)) return false;
-      if (TextObservation != other.TextObservation) return false;
-      if (StoredTextActions != other.StoredTextActions) return false;
-      if (!object.Equals(CustomObservation, other.CustomObservation)) return false;
       if(!compressedObservations_.Equals(other.compressedObservations_)) return false;
       return Equals(_unknownFields, other._unknownFields);
     }
       int hash = 1;
       hash ^= stackedVectorObservation_.GetHashCode();
-      if (TextObservation.Length != 0) hash ^= TextObservation.GetHashCode();
-      if (StoredTextActions.Length != 0) hash ^= StoredTextActions.GetHashCode();
-      if (customObservation_ != null) hash ^= CustomObservation.GetHashCode();
       hash ^= compressedObservations_.GetHashCode();
       if (_unknownFields != null) {
         hash ^= _unknownFields.GetHashCode();
     [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
     public void WriteTo(pb::CodedOutputStream output) {
       stackedVectorObservation_.WriteTo(output, _repeated_stackedVectorObservation_codec);
-      if (TextObservation.Length != 0) {
-        output.WriteRawTag(26);
-        output.WriteString(TextObservation);
-      }
-      if (StoredTextActions.Length != 0) {
-        output.WriteRawTag(42);
-        output.WriteString(StoredTextActions);
-      }
       if (Reward != 0F) {
         output.WriteRawTag(61);
         output.WriteFloat(Reward);
         output.WriteInt32(Id);
       }
       actionMask_.WriteTo(output, _repeated_actionMask_codec);
-      if (customObservation_ != null) {
-        output.WriteRawTag(98);
-        output.WriteMessage(CustomObservation);
-      }
       compressedObservations_.WriteTo(output, _repeated_compressedObservations_codec);
       if (_unknownFields != null) {
         _unknownFields.WriteTo(output);
     public int CalculateSize() {
       int size = 0;
       size += stackedVectorObservation_.CalculateSize(_repeated_stackedVectorObservation_codec);
-      if (TextObservation.Length != 0) {
-        size += 1 + pb::CodedOutputStream.ComputeStringSize(TextObservation);
-      }
-      if (StoredTextActions.Length != 0) {
-        size += 1 + pb::CodedOutputStream.ComputeStringSize(StoredTextActions);
-      }
       if (Reward != 0F) {
         size += 1 + 4;
       }
         size += 1 + pb::CodedOutputStream.ComputeInt32Size(Id);
       }
       size += actionMask_.CalculateSize(_repeated_actionMask_codec);
-      if (customObservation_ != null) {
-        size += 1 + pb::CodedOutputStream.ComputeMessageSize(CustomObservation);
-      }
       size += compressedObservations_.CalculateSize(_repeated_compressedObservations_codec);
       if (_unknownFields != null) {
         size += _unknownFields.CalculateSize();
         return;
       }
       stackedVectorObservation_.Add(other.stackedVectorObservation_);
-      if (other.TextObservation.Length != 0) {
-        TextObservation = other.TextObservation;
-      }
-      if (other.StoredTextActions.Length != 0) {
-        StoredTextActions = other.StoredTextActions;
-      }
       if (other.Reward != 0F) {
         Reward = other.Reward;
       }
         Id = other.Id;
       }
       actionMask_.Add(other.actionMask_);
-      if (other.customObservation_ != null) {
-        if (customObservation_ == null) {
-          customObservation_ = new global::MLAgents.CommunicatorObjects.CustomObservationProto();
-        }
-        CustomObservation.MergeFrom(other.CustomObservation);
-      }
       compressedObservations_.Add(other.compressedObservations_);
       _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields);
     }
             stackedVectorObservation_.AddEntriesFrom(input, _repeated_stackedVectorObservation_codec);
             break;
           }
-          case 26: {
-            TextObservation = input.ReadString();
-            break;
-          }
-            break;
-          }
-          case 42: {
-            StoredTextActions = input.ReadString();
             break;
           }
           case 61: {
           case 90:
           case 88: {
             actionMask_.AddEntriesFrom(input, _repeated_actionMask_codec);
-            break;
-          }
-          case 98: {
-            if (customObservation_ == null) {
-              customObservation_ = new global::MLAgents.CommunicatorObjects.CustomObservationProto();
-            }
-            input.ReadMessage(customObservation_);
             break;
           }
           case 106: {

UnitySDK/Assets/ML-Agents/Scripts/Grpc/GrpcExtensions.cs

             {
                 StackedVectorObservation = { ai.floatObservations },
                 StoredVectorActions = { ai.storedVectorActions },
-                StoredTextActions = ai.storedTextActions,
-                TextObservation = ai.textObservation,
-                CustomObservation = ai.customObservation
             };
 
             if (ai.actionMasks != null)
             return new AgentAction
             {
                 vectorActions = aap.VectorActions.ToArray(),
-                textActions = aap.TextActions,
-                customAction = aap.CustomAction
             };
         }
 

docs/Glossary.md

 * **Frame** - An instance of rendering the main camera for the display.
   Corresponds to each `Update` call of the game engine.
 * **Observation** - Partial information describing the state of the environment
-  available to a given agent. (e.g. Vector, Visual, Text)
+  available to a given agent. (e.g. Vector, Visual)
 * **Policy** - Function for producing decisions from observations.
 * **Reward** - Signal provided at every step used to indicate desirability of an
   agent’s action within the current state of the environment.

docs/Learning-Environment-Create-New.md

 
 ```csharp
 public float speed = 10;
-public override void AgentAction(float[] vectorAction, string textAction)
+public override void AgentAction(float[] vectorAction)
 {
     // Actions, size = 2
     Vector3 controlSignal = Vector3.zero;

docs/Python-API.md

   the list corresponds to the n<sup>th</sup> observation of the Brain.
 - **`vector_observations`** : A two dimensional numpy array of dimension `(batch
   size, vector observation size)`.
-- **`text_observations`** : A list of string corresponding to the Agents text
-  observations.
 - **`memories`** : A two dimensional numpy array of dimension `(batch size,
   memory size)` which corresponds to the memories sent at the previous step.
 - **`rewards`** : A list as long as the number of Agents using the Brain
     `resetParameters` and the values are their corresponding float values.
     Define the reset parameters on the Academy Inspector window in the Unity
     Editor.
-- **Step : `env.step(action, memory=None, text_action=None)`**
+- **Step : `env.step(action)`**
-  - `memory` is an optional input that can be used to send a list of floats per
-    Agents to be retrieved at the next step.
-  - `text_action` is an optional input that be used to send a single string per
-    Agent.
 
     Returns a dictionary mapping Brain names to BrainInfo objects.
 

docs/Readme.md

   * [Using the Monitor](Feature-Monitor.md)
   * [Using the Video Recorder](https://github.com/Unity-Technologies/video-recorder)
   * [Using an Executable Environment](Learning-Environment-Executable.md)
-  * [Creating Custom Protobuf Messages](Creating-Custom-Protobuf-Messages.md)
 
 ## Training
 
 * [API Reference](API-Reference.md)
 * [How to use the Python API](Python-API.md)
 * [Wrapping Learning Environment as a Gym (+Baselines/Dopamine Integration)](../gym-unity/README.md)
-* [Creating custom protobuf messages](Creating-Custom-Protobuf-Messages.md)

gym-unity/gym_unity/envs/__init__.py

             default_observation,
             info.rewards[0],
             info.local_done[0],
-            {"text_observation": info.text_observations[0], "brain_info": info},
+            {"text_observation": None, "brain_info": info},
         )
 
     def _preprocess_single(self, single_visual_obs):
             list(default_observation),
             info.rewards,
             info.local_done,
-            {"text_observation": info.text_observations, "brain_info": info},
+            {"text_observation": None, "brain_info": info},
         )
 
     def _preprocess_multi(self, multiple_visual_obs):

gym-unity/gym_unity/tests/test_gym.py

         mock_braininfo.return_value.visual_observations = [[np.zeros(shape=(8, 8, 3))]]
     mock_braininfo.return_value.rewards = num_agents * [1.0]
     mock_braininfo.return_value.local_done = num_agents * [False]
-    mock_braininfo.return_value.text_observations = num_agents * [""]
     mock_braininfo.return_value.agents = range(0, num_agents)
     return mock_braininfo()
 

ml-agents-envs/mlagents/envs/action_info.py

 
 class ActionInfo(NamedTuple):
     action: Any
-    text: Any
     value: Any
     outputs: ActionInfoOutputs

ml-agents-envs/mlagents/envs/base_unity_environment.py

 class BaseUnityEnvironment(ABC):
     @abstractmethod
     def step(
-        self,
-        vector_action: Optional[Dict] = None,
-        text_action: Optional[Dict] = None,
-        value: Optional[Dict] = None,
-        custom_action: Dict[str, Any] = None,
+        self, vector_action: Optional[Dict] = None, value: Optional[Dict] = None
     ) -> AllBrainInfo:
         pass
 

ml-agents-envs/mlagents/envs/brain.py

         self,
         visual_observation,
         vector_observation,
-        text_observations,
-        text_action=None,
-        custom_observations=None,
     ):
         """
         Describes experience at current step of all agents linked to a brain.
-        self.text_observations = text_observations
-        self.previous_text_actions = text_action
-        self.custom_observations = custom_observations
 
     @staticmethod
     def merge_memories(m1, m2, agents1, agents2):
         brain_info = BrainInfo(
             visual_observation=vis_obs,
             vector_observation=vector_obs,
-            text_observations=[x.text_observation for x in agent_info_list],
-            text_action=[list(x.stored_text_actions) for x in agent_info_list],
-            custom_observations=[x.custom_observation for x in agent_info_list],
             action_mask=mask_actions,
         )
         return brain_info

ml-agents-envs/mlagents/envs/communicator_objects/agent_action_pb2.py

 _sym_db = _symbol_database.Default()
 
 
-from mlagents.envs.communicator_objects import custom_action_pb2 as mlagents_dot_envs_dot_communicator__objects_dot_custom__action__pb2
 
 
 DESCRIPTOR = _descriptor.FileDescriptor(
-  serialized_pb=_b('\n5mlagents/envs/communicator_objects/agent_action.proto\x12\x14\x63ommunicator_objects\x1a\x36mlagents/envs/communicator_objects/custom_action.proto\"\x95\x01\n\x10\x41gentActionProto\x12\x16\n\x0evector_actions\x18\x01 \x03(\x02\x12\x14\n\x0ctext_actions\x18\x02 \x01(\t\x12\r\n\x05value\x18\x04 \x01(\x02\x12>\n\rcustom_action\x18\x05 \x01(\x0b\x32\'.communicator_objects.CustomActionProtoJ\x04\x08\x03\x10\x04\x42\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
-  ,
-  dependencies=[mlagents_dot_envs_dot_communicator__objects_dot_custom__action__pb2.DESCRIPTOR,])
+  serialized_pb=_b('\n5mlagents/envs/communicator_objects/agent_action.proto\x12\x14\x63ommunicator_objects\"K\n\x10\x41gentActionProto\x12\x16\n\x0evector_actions\x18\x01 \x03(\x02\x12\r\n\x05value\x18\x04 \x01(\x02J\x04\x08\x02\x10\x03J\x04\x08\x03\x10\x04J\x04\x08\x05\x10\x06\x42\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
+)
 
 
 
       is_extension=False, extension_scope=None,
       options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
-      name='text_actions', full_name='communicator_objects.AgentActionProto.text_actions', index=1,
-      number=2, type=9, cpp_type=9, label=1,
-      has_default_value=False, default_value=_b("").decode('utf-8'),
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='value', full_name='communicator_objects.AgentActionProto.value', index=2,
+      name='value', full_name='communicator_objects.AgentActionProto.value', index=1,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='custom_action', full_name='communicator_objects.AgentActionProto.custom_action', index=3,
-      number=5, type=11, cpp_type=10, label=1,
-      has_default_value=False, default_value=None,
       message_type=None, enum_type=None, containing_type=None,
       is_extension=False, extension_scope=None,
       options=None, file=DESCRIPTOR),
   extension_ranges=[],
   oneofs=[
   ],
-  serialized_start=136,
-  serialized_end=285,
+  serialized_start=79,
+  serialized_end=154,
-_AGENTACTIONPROTO.fields_by_name['custom_action'].message_type = mlagents_dot_envs_dot_communicator__objects_dot_custom__action__pb2._CUSTOMACTIONPROTO
 DESCRIPTOR.message_types_by_name['AgentActionProto'] = _AGENTACTIONPROTO
 _sym_db.RegisterFileDescriptor(DESCRIPTOR)
 

ml-agents-envs/mlagents/envs/communicator_objects/agent_action_pb2.pyi

     Message as google___protobuf___message___Message,
 )
 
-from mlagents.envs.communicator_objects.custom_action_pb2 import (
-    CustomActionProto as mlagents___envs___communicator_objects___custom_action_pb2___CustomActionProto,
-)
-
-    Text as typing___Text,
 )
 
 from typing_extensions import (
 class AgentActionProto(google___protobuf___message___Message):
     DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
     vector_actions = ... # type: google___protobuf___internal___containers___RepeatedScalarFieldContainer[builtin___float]
-    text_actions = ... # type: typing___Text
-
-    @property
-    def custom_action(self) -> mlagents___envs___communicator_objects___custom_action_pb2___CustomActionProto: ...
-        text_actions : typing___Optional[typing___Text] = None,
-        custom_action : typing___Optional[mlagents___envs___communicator_objects___custom_action_pb2___CustomActionProto] = None,
         ) -> None: ...
     @classmethod
     def FromString(cls, s: builtin___bytes) -> AgentActionProto: ...
-        def HasField(self, field_name: typing_extensions___Literal[u"custom_action"]) -> builtin___bool: ...
-        def ClearField(self, field_name: typing_extensions___Literal[u"custom_action",u"text_actions",u"value",u"vector_actions"]) -> None: ...
+        def ClearField(self, field_name: typing_extensions___Literal[u"value",u"vector_actions"]) -> None: ...
-        def HasField(self, field_name: typing_extensions___Literal[u"custom_action",b"custom_action"]) -> builtin___bool: ...
-        def ClearField(self, field_name: typing_extensions___Literal[u"custom_action",b"custom_action",u"text_actions",b"text_actions",u"value",b"value",u"vector_actions",b"vector_actions"]) -> None: ...
+        def ClearField(self, field_name: typing_extensions___Literal[u"value",b"value",u"vector_actions",b"vector_actions"]) -> None: ...

ml-agents-envs/mlagents/envs/communicator_objects/agent_info_pb2.py

 
 
 from mlagents.envs.communicator_objects import compressed_observation_pb2 as mlagents_dot_envs_dot_communicator__objects_dot_compressed__observation__pb2
-from mlagents.envs.communicator_objects import custom_observation_pb2 as mlagents_dot_envs_dot_communicator__objects_dot_custom__observation__pb2
 
 
 DESCRIPTOR = _descriptor.FileDescriptor(
-  serialized_pb=_b('\n3mlagents/envs/communicator_objects/agent_info.proto\x12\x14\x63ommunicator_objects\x1a?mlagents/envs/communicator_objects/compressed_observation.proto\x1a;mlagents/envs/communicator_objects/custom_observation.proto\"\x8c\x03\n\x0e\x41gentInfoProto\x12\"\n\x1astacked_vector_observation\x18\x01 \x03(\x02\x12\x18\n\x10text_observation\x18\x03 \x01(\t\x12\x1d\n\x15stored_vector_actions\x18\x04 \x03(\x02\x12\x1b\n\x13stored_text_actions\x18\x05 \x01(\t\x12\x0e\n\x06reward\x18\x07 \x01(\x02\x12\x0c\n\x04\x64one\x18\x08 \x01(\x08\x12\x18\n\x10max_step_reached\x18\t \x01(\x08\x12\n\n\x02id\x18\n \x01(\x05\x12\x13\n\x0b\x61\x63tion_mask\x18\x0b \x03(\x08\x12H\n\x12\x63ustom_observation\x18\x0c \x01(\x0b\x32,.communicator_objects.CustomObservationProto\x12Q\n\x17\x63ompressed_observations\x18\r \x03(\x0b\x32\x30.communicator_objects.CompressedObservationProtoJ\x04\x08\x02\x10\x03J\x04\x08\x06\x10\x07\x42\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
+  serialized_pb=_b('\n3mlagents/envs/communicator_objects/agent_info.proto\x12\x14\x63ommunicator_objects\x1a?mlagents/envs/communicator_objects/compressed_observation.proto\"\x9d\x02\n\x0e\x41gentInfoProto\x12\"\n\x1astacked_vector_observation\x18\x01 \x03(\x02\x12\x1d\n\x15stored_vector_actions\x18\x04 \x03(\x02\x12\x0e\n\x06reward\x18\x07 \x01(\x02\x12\x0c\n\x04\x64one\x18\x08 \x01(\x08\x12\x18\n\x10max_step_reached\x18\t \x01(\x08\x12\n\n\x02id\x18\n \x01(\x05\x12\x13\n\x0b\x61\x63tion_mask\x18\x0b \x03(\x08\x12Q\n\x17\x63ompressed_observations\x18\r \x03(\x0b\x32\x30.communicator_objects.CompressedObservationProtoJ\x04\x08\x02\x10\x03J\x04\x08\x03\x10\x04J\x04\x08\x05\x10\x06J\x04\x08\x06\x10\x07J\x04\x08\x0c\x10\rB\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
-  dependencies=[mlagents_dot_envs_dot_communicator__objects_dot_compressed__observation__pb2.DESCRIPTOR,mlagents_dot_envs_dot_communicator__objects_dot_custom__observation__pb2.DESCRIPTOR,])
+  dependencies=[mlagents_dot_envs_dot_communicator__objects_dot_compressed__observation__pb2.DESCRIPTOR,])
 
 
 
       is_extension=False, extension_scope=None,
       options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
-      name='text_observation', full_name='communicator_objects.AgentInfoProto.text_observation', index=1,
-      number=3, type=9, cpp_type=9, label=1,
-      has_default_value=False, default_value=_b("").decode('utf-8'),
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='stored_vector_actions', full_name='communicator_objects.AgentInfoProto.stored_vector_actions', index=2,
+      name='stored_vector_actions', full_name='communicator_objects.AgentInfoProto.stored_vector_actions', index=1,
       number=4, type=2, cpp_type=6, label=3,
       has_default_value=False, default_value=[],
       message_type=None, enum_type=None, containing_type=None,
-      name='stored_text_actions', full_name='communicator_objects.AgentInfoProto.stored_text_actions', index=3,
-      number=5, type=9, cpp_type=9, label=1,
-      has_default_value=False, default_value=_b("").decode('utf-8'),
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='reward', full_name='communicator_objects.AgentInfoProto.reward', index=4,
+      name='reward', full_name='communicator_objects.AgentInfoProto.reward', index=2,
       number=7, type=2, cpp_type=6, label=1,
       has_default_value=False, default_value=float(0),
       message_type=None, enum_type=None, containing_type=None,
-      name='done', full_name='communicator_objects.AgentInfoProto.done', index=5,
+      name='done', full_name='communicator_objects.AgentInfoProto.done', index=3,
       number=8, type=8, cpp_type=7, label=1,
       has_default_value=False, default_value=False,
       message_type=None, enum_type=None, containing_type=None,
-      name='max_step_reached', full_name='communicator_objects.AgentInfoProto.max_step_reached', index=6,
+      name='max_step_reached', full_name='communicator_objects.AgentInfoProto.max_step_reached', index=4,
       number=9, type=8, cpp_type=7, label=1,
       has_default_value=False, default_value=False,
       message_type=None, enum_type=None, containing_type=None,
-      name='id', full_name='communicator_objects.AgentInfoProto.id', index=7,
+      name='id', full_name='communicator_objects.AgentInfoProto.id', index=5,
       number=10, type=5, cpp_type=1, label=1,
       has_default_value=False, default_value=0,
       message_type=None, enum_type=None, containing_type=None,
-      name='action_mask', full_name='communicator_objects.AgentInfoProto.action_mask', index=8,
+      name='action_mask', full_name='communicator_objects.AgentInfoProto.action_mask', index=6,
       number=11, type=8, cpp_type=7, label=3,
       has_default_value=False, default_value=[],
       message_type=None, enum_type=None, containing_type=None,
-      name='custom_observation', full_name='communicator_objects.AgentInfoProto.custom_observation', index=9,
-      number=12, type=11, cpp_type=10, label=1,
-      has_default_value=False, default_value=None,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='compressed_observations', full_name='communicator_objects.AgentInfoProto.compressed_observations', index=10,
+      name='compressed_observations', full_name='communicator_objects.AgentInfoProto.compressed_observations', index=7,
       number=13, type=11, cpp_type=10, label=3,
       has_default_value=False, default_value=[],
       message_type=None, enum_type=None, containing_type=None,
   extension_ranges=[],
   oneofs=[
   ],
-  serialized_start=204,
-  serialized_end=600,
+  serialized_start=143,
+  serialized_end=428,
-_AGENTINFOPROTO.fields_by_name['custom_observation'].message_type = mlagents_dot_envs_dot_communicator__objects_dot_custom__observation__pb2._CUSTOMOBSERVATIONPROTO
 _AGENTINFOPROTO.fields_by_name['compressed_observations'].message_type = mlagents_dot_envs_dot_communicator__objects_dot_compressed__observation__pb2._COMPRESSEDOBSERVATIONPROTO
 DESCRIPTOR.message_types_by_name['AgentInfoProto'] = _AGENTINFOPROTO
 _sym_db.RegisterFileDescriptor(DESCRIPTOR)

ml-agents-envs/mlagents/envs/communicator_objects/agent_info_pb2.pyi

     CompressedObservationProto as mlagents___envs___communicator_objects___compressed_observation_pb2___CompressedObservationProto,
 )
 
-from mlagents.envs.communicator_objects.custom_observation_pb2 import (
-    CustomObservationProto as mlagents___envs___communicator_objects___custom_observation_pb2___CustomObservationProto,
-)
-
-    Text as typing___Text,
 )
 
 from typing_extensions import (
 class AgentInfoProto(google___protobuf___message___Message):
     DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
     stacked_vector_observation = ... # type: google___protobuf___internal___containers___RepeatedScalarFieldContainer[builtin___float]
-    text_observation = ... # type: typing___Text
-    stored_text_actions = ... # type: typing___Text
-
-    @property
-    def custom_observation(self) -> mlagents___envs___communicator_objects___custom_observation_pb2___CustomObservationProto: ...
 
     @property
     def compressed_observations(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[mlagents___envs___communicator_objects___compressed_observation_pb2___CompressedObservationProto]: ...
         stacked_vector_observation : typing___Optional[typing___Iterable[builtin___float]] = None,
-        text_observation : typing___Optional[typing___Text] = None,
-        stored_text_actions : typing___Optional[typing___Text] = None,
-        custom_observation : typing___Optional[mlagents___envs___communicator_objects___custom_observation_pb2___CustomObservationProto] = None,
         compressed_observations : typing___Optional[typing___Iterable[mlagents___envs___communicator_objects___compressed_observation_pb2___CompressedObservationProto]] = None,
         ) -> None: ...
     @classmethod
     if sys.version_info >= (3,):
-        def HasField(self, field_name: typing_extensions___Literal[u"custom_observation"]) -> builtin___bool: ...
-        def ClearField(self, field_name: typing_extensions___Literal[u"action_mask",u"compressed_observations",u"custom_observation",u"done",u"id",u"max_step_reached",u"reward",u"stacked_vector_observation",u"stored_text_actions",u"stored_vector_actions",u"text_observation"]) -> None: ...
+        def ClearField(self, field_name: typing_extensions___Literal[u"action_mask",u"compressed_observations",u"done",u"id",u"max_step_reached",u"reward",u"stacked_vector_observation",u"stored_vector_actions"]) -> None: ...
-        def HasField(self, field_name: typing_extensions___Literal[u"custom_observation",b"custom_observation"]) -> builtin___bool: ...
-        def ClearField(self, field_name: typing_extensions___Literal[u"action_mask",b"action_mask",u"compressed_observations",b"compressed_observations",u"custom_observation",b"custom_observation",u"done",b"done",u"id",b"id",u"max_step_reached",b"max_step_reached",u"reward",b"reward",u"stacked_vector_observation",b"stacked_vector_observation",u"stored_text_actions",b"stored_text_actions",u"stored_vector_actions",b"stored_vector_actions",u"text_observation",b"text_observation"]) -> None: ...
+        def ClearField(self, field_name: typing_extensions___Literal[u"action_mask",b"action_mask",u"compressed_observations",b"compressed_observations",u"done",b"done",u"id",b"id",u"max_step_reached",b"max_step_reached",u"reward",b"reward",u"stacked_vector_observation",b"stacked_vector_observation",u"stored_vector_actions",b"stored_vector_actions"]) -> None: ...

ml-agents-envs/mlagents/envs/environment.py

 )
 
 from mlagents.envs.communicator_objects.unity_input_pb2 import UnityInputProto
-from mlagents.envs.communicator_objects.custom_action_pb2 import CustomActionProto
 
 from .rpc_communicator import RpcCommunicator
 from sys import platform
 class UnityEnvironment(BaseUnityEnvironment):
     SCALAR_ACTION_TYPES = (int, np.int32, np.int64, float, np.float32, np.float64)
     SINGLE_BRAIN_ACTION_TYPES = SCALAR_ACTION_TYPES + (list, np.ndarray)
-    SINGLE_BRAIN_TEXT_TYPES = list
     API_VERSION = "API-11"
 
     def __init__(
     def step(
         self,
         vector_action: Dict[str, np.ndarray] = None,
-        text_action: Optional[Dict[str, List[str]]] = None,
-        custom_action: Dict[str, Any] = None,
     ) -> AllBrainInfo:
         """
         Provides the environment with an action, moves the environment dynamics forward accordingly,
         :param memory: Vector corresponding to memory used for recurrent policies.
-        :param text_action: Text action to send to environment for.
-        :param custom_action: Optional instance of a CustomAction protobuf message.
-        text_action = {} if text_action is None else text_action
-        custom_action = {} if custom_action is None else custom_action
 
         # Check that environment is loaded, and episode is currently running.
         if not self._loaded:
                         "step cannot take a vector_action input"
                     )
 
-            if isinstance(text_action, self.SINGLE_BRAIN_TEXT_TYPES):
-                if self._num_external_brains == 1:
-                    text_action = {self._external_brain_names[0]: text_action}
-                elif self._num_external_brains > 1:
-                    raise UnityActionException(
-                        "You have {0} brains, you need to feed a dictionary of brain names as keys "
-                        "and text_actions as values".format(self._num_external_brains)
-                    )
-                else:
-                    raise UnityActionException(
-                        "There are no external brains in the environment, "
-                        "step cannot take a value input"
-                    )
-
             if isinstance(value, self.SINGLE_BRAIN_ACTION_TYPES):
                 if self._num_external_brains == 1:
                     value = {self._external_brain_names[0]: value}
                         "step cannot take a value input"
                     )
 
-            if isinstance(custom_action, CustomActionProto):
-                if self._num_external_brains == 1:
-                    custom_action = {self._external_brain_names[0]: custom_action}
-                elif self._num_external_brains > 1:
-                    raise UnityActionException(
-                        "You have {0} brains, you need to feed a dictionary of brain names as keys "
-                        "and CustomAction instances as values".format(
-                            self._num_external_brains
-                        )
-                    )
-                else:
-                    raise UnityActionException(
-                        "There are no external brains in the environment, "
-                        "step cannot take a custom_action input"
-                    )
-
-            for brain_name in list(vector_action.keys()) + list(text_action.keys()):
+            for brain_name in list(vector_action.keys()):
                 if brain_name not in self._external_brain_names:
                     raise UnityActionException(
                         "The name {0} does not correspond to an external brain "
                         )
                 else:
                     vector_action[brain_name] = self._flatten(vector_action[brain_name])
-                if brain_name not in text_action:
-                    text_action[brain_name] = [""] * n_agent
-                else:
-                    if text_action[brain_name] is None:
-                        text_action[brain_name] = [""] * n_agent
-                if brain_name not in custom_action:
-                    custom_action[brain_name] = [None] * n_agent
-                else:
-                    if custom_action[brain_name] is None:
-                        custom_action[brain_name] = [None] * n_agent
-                    if isinstance(custom_action[brain_name], CustomActionProto):
-                        custom_action[brain_name] = [
-                            custom_action[brain_name]
-                        ] * n_agent
-
-                number_text_actions = len(text_action[brain_name])
-                if not ((number_text_actions == n_agent) or number_text_actions == 0):
-                    raise UnityActionException(
-                        "There was a mismatch between the provided text_action and "
-                        "the environment's expectation: "
-                        "The brain {0} expected {1} text_action but was given {2}".format(
-                            brain_name, n_agent, number_text_actions
-                        )
-                    )
 
                 discrete_check = (
                     self._brains[brain_name].vector_action_space_type == "discrete"
                         )
                     )
 
-            step_input = self._generate_step_input(
-                vector_action, text_action, value, custom_action
-            )
+            step_input = self._generate_step_input(vector_action, value)
             with hierarchical_timer("communicator.exchange"):
                 outputs = self.communicator.exchange(step_input)
             if outputs is None: