merge master into trainer-plugin branch

DevProject/Packages/manifest.json

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     "com.unity.2d.sprite": "1.0.0",
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-    "com.unity.ads": "3.4.4",
+    "com.unity.ads": "3.4.7",
-    "com.unity.ide.vscode": "1.1.4",
+    "com.unity.ide.vscode": "1.2.1",
-    "com.unity.package-validation-suite": "0.11.0-preview",
+    "com.unity.package-validation-suite": "0.14.0-preview",
-    "com.unity.test-framework": "1.1.13",
+    "com.unity.test-framework": "1.1.14",
-    "com.unity.xr.legacyinputhelpers": "1.3.11",
+    "com.unity.xr.legacyinputhelpers": "2.1.4",
     "com.unity.modules.ai": "1.0.0",
     "com.unity.modules.androidjni": "1.0.0",
     "com.unity.modules.animation": "1.0.0",

DevProject/ProjectSettings/ProjectVersion.txt

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Project/Assets/ML-Agents/Examples/Basic/Prefabs/Basic.prefab

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Project/Assets/ML-Agents/Examples/Basic/Scenes/Basic.unity

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Project/Assets/ML-Agents/Examples/Basic/Scripts/BasicController.cs

     /// <summary>
     /// Controls the movement of the GameObject based on the actions received.
     /// </summary>
-    /// <param name="vectorAction"></param>
-    public void ApplyAction(ActionSegment<int> vectorAction)
+    /// <param name="direction"></param>
+    public void MoveDirection(int direction)
-        var movement = vectorAction[0];
-
-        var direction = 0;
-
-        switch (movement)
-        {
-            case 1:
-                direction = -1;
-                break;
-            case 2:
-                direction = 1;
-                break;
-        }
-
         position += direction;
         if (position < k_MinPosition) { position = k_MinPosition; }
         if (position > k_MaxPosition) { position = k_MaxPosition; }
         }
         if (Academy.Instance.IsCommunicatorOn)
         {
-            // Apply the previous step's actions
-            ApplyAction(m_Agent.GetStoredActionBuffers().DiscreteActions);
             m_Agent?.RequestDecision();
         }
         else
-                // Apply the previous step's actions
-                ApplyAction(m_Agent.GetStoredActionBuffers().DiscreteActions);
-
                 m_TimeSinceDecision = 0f;
                 m_Agent?.RequestDecision();
             }

com.unity.ml-agents/CHANGELOG.md

 
 ### Minor Changes
 #### com.unity.ml-agents (C#)
-#### ml-agents / ml-agents-envs / gym-unity (Python)
-
-### Bug Fixes
-#### com.unity.ml-agents (C#)
-#### ml-agents / ml-agents-envs / gym-unity (Python)
-
-## [1.3.0-preview] - 2020-08-12
-
-### Major Changes
-#### com.unity.ml-agents (C#)
-#### ml-agents / ml-agents-envs / gym-unity (Python)
-
-### Minor Changes
-#### com.unity.ml-agents (C#)
 - Update Barracuda to 1.0.2.
 - Enabled C# formatting using `dotnet-format`.
 #### ml-agents / ml-agents-envs / gym-unity (Python)
+- The package dependencies were updated to include the built-in packages that are used also. (#4384)
 #### ml-agents / ml-agents-envs / gym-unity (Python)
 
 ## [1.3.0-preview] - 2020-08-12
 - The interaction between EnvManager and TrainerController was changed; EnvManager.advance() was split into to stages,
 and TrainerController now uses the results from the first stage to handle new behavior names. This change speeds up
 Python training by approximately 5-10%. (#4259)
+- Experimental PyTorch support has been added. Use `--torch` when running `mlagents-learn`, or add
+`framework: pytorch` to your trainer configuration (under the behavior name) to enable it.
+Note that PyTorch 1.6.0 or greater should be installed to use this feature; see
+[the PyTorch website](https://pytorch.org/) for installation instructions. (#4335)
 
 ### Minor Changes
 #### com.unity.ml-agents (C#)

com.unity.ml-agents/Editor/BehaviorParametersEditor.cs

 using System.Collections.Generic;
 using UnityEditor;
 using Unity.Barracuda;
+using Unity.MLAgents.Actuators;
 using Unity.MLAgents.Policies;
 using Unity.MLAgents.Sensors;
 using Unity.MLAgents.Sensors.Reflection;
             var behaviorParameters = (BehaviorParameters)target;
 
             // Grab the sensor components, since we need them to determine the observation sizes.
+            // TODO make these methods of BehaviorParameters
             SensorComponent[] sensorComponents;
             if (behaviorParameters.UseChildSensors)
             {
             {
                 sensorComponents = behaviorParameters.GetComponents<SensorComponent>();
+            }
+
+            ActuatorComponent[] actuatorComponents;
+            if (behaviorParameters.UseChildActuators)
+            {
+                actuatorComponents = behaviorParameters.GetComponentsInChildren<ActuatorComponent>();
+            }
+            else
+            {
+                actuatorComponents = behaviorParameters.GetComponents<ActuatorComponent>();
             }
 
             // Get the total size of the sensors generated by ObservableAttributes.
             if (brainParameters != null)
             {
                 var failedChecks = Inference.BarracudaModelParamLoader.CheckModel(
-                    barracudaModel, brainParameters, sensorComponents,
+                    barracudaModel, brainParameters, sensorComponents, actuatorComponents,
                     observableAttributeSensorTotalSize, behaviorParameters.BehaviorType
                 );
                 foreach (var check in failedChecks)

com.unity.ml-agents/Runtime/Academy.cs

 #if UNITY_EDITOR
 using UnityEditor;
 #endif
+using Unity.MLAgents.Actuators;
 using Unity.MLAgents.Inference;
 using Unity.MLAgents.Policies;
 using Unity.MLAgents.SideChannels;
         /// NNModel and the InferenceDevice as provided.
         /// </summary>
         /// <param name="model">The NNModel the ModelRunner must use.</param>
-        /// <param name="brainParameters">The BrainParameters used to create the ModelRunner.</param>
+        /// <param name="actionSpec"> Description of the action spaces for the Agent.</param>
-            NNModel model, BrainParameters brainParameters, InferenceDevice inferenceDevice)
+            NNModel model, ActionSpec actionSpec, InferenceDevice inferenceDevice)
-                modelRunner = new ModelRunner(model, brainParameters, inferenceDevice, m_InferenceSeed);
+                modelRunner = new ModelRunner(model, actionSpec, inferenceDevice, m_InferenceSeed);
                 m_ModelRunners.Add(modelRunner);
                 m_InferenceSeed++;
             }

com.unity.ml-agents/Runtime/Actuators/ActionSpec.cs

         /// <param name="branchSizes">The array of branch sizes for the discrete action space.  Each index
         /// contains the number of actions available for that branch.</param>
         /// <returns>An Discrete ActionSpec initialized with the array of branch sizes.</returns>
-        public static ActionSpec MakeDiscrete(int[] branchSizes)
+        public static ActionSpec MakeDiscrete(params int[] branchSizes)
         {
             var numActions = branchSizes.Length;
             var actuatorSpace = new ActionSpec(0, numActions, branchSizes);
-        ActionSpec(int numContinuousActions, int numDiscreteActions, int[] branchSizes = null)
+        internal ActionSpec(int numContinuousActions, int numDiscreteActions, int[] branchSizes = null)
+        }
+
+        /// <summary>
+        /// Temporary check that the ActionSpec uses either all continuous or all discrete actions.
+        /// This should be removed once the trainer supports them.
+        /// </summary>
+        /// <exception cref="UnityAgentsException"></exception>
+        internal void CheckNotHybrid()
+        {
+            if (NumContinuousActions > 0 && NumDiscreteActions > 0)
+            {
+                throw new UnityAgentsException("ActionSpecs must be all continuous or all discrete.");
+            }
         }
     }
 }

com.unity.ml-agents/Runtime/Actuators/ActuatorComponent.cs

     /// Editor components for creating Actuators. Generally an IActuator component should
     /// have a corresponding ActuatorComponent.
     /// </summary>
-    internal abstract class ActuatorComponent : MonoBehaviour
+    public abstract class ActuatorComponent : MonoBehaviour
     {
         /// <summary>
         /// Create the IActuator.  This is called by the Agent when it is initialized.
+
+        /// <summary>
+        /// The specification of the Action space for this ActuatorComponent.
+        /// This must produce the same results as the corresponding IActuator's ActionSpec.
+        /// </summary>
+        /// <seealso cref="ActionSpec"/>
+        public abstract ActionSpec ActionSpec { get; }
     }
 }

com.unity.ml-agents/Runtime/Actuators/ActuatorDiscreteActionMask.cs

         /// </summary>
         public int CurrentBranchOffset { get; set; }
 
-        internal ActuatorDiscreteActionMask(IList<IActuator> actuators, int sumOfDiscreteBranchSizes, int numBranches)
+        internal ActuatorDiscreteActionMask(IList<IActuator> actuators, int sumOfDiscreteBranchSizes, int numBranches, int[] branchSizes = null)
+            m_BranchSizes = branchSizes;
         }
 
         /// <inheritdoc cref="IDiscreteActionMask.WriteMask"/>

com.unity.ml-agents/Runtime/Actuators/ActuatorManager.cs

         // An implementation of IDiscreteActionMask that allows for writing to it based on an offset.
         ActuatorDiscreteActionMask m_DiscreteActionMask;
 
+        ActionSpec m_CombinedActionSpec;
+
         /// <summary>
         /// Flag used to check if our IActuators are ready for execution.
         /// </summary>
             var discreteActions = numDiscreteBranches == 0 ? ActionSegment<int>.Empty : new ActionSegment<int>(new int[numDiscreteBranches]);
 
             StoredActions = new ActionBuffers(continuousActions, discreteActions);
-            m_DiscreteActionMask = new ActuatorDiscreteActionMask(actuators, sumOfDiscreteBranches, numDiscreteBranches);
+            m_CombinedActionSpec = CombineActionSpecs(actuators);
+            m_DiscreteActionMask = new ActuatorDiscreteActionMask(actuators, sumOfDiscreteBranches, numDiscreteBranches, m_CombinedActionSpec.BranchSizes);
+        }
+
+        internal static ActionSpec CombineActionSpecs(IList<IActuator> actuators)
+        {
+            int numContinuousActions = 0;
+            int numDiscreteActions = 0;
+
+            foreach (var actuator in actuators)
+            {
+                numContinuousActions += actuator.ActionSpec.NumContinuousActions;
+                numDiscreteActions += actuator.ActionSpec.NumDiscreteActions;
+            }
+
+            int[] combinedBranchSizes;
+            if (numDiscreteActions == 0)
+            {
+                combinedBranchSizes = Array.Empty<int>();
+            }
+            else
+            {
+                combinedBranchSizes = new int[numDiscreteActions];
+                var start = 0;
+                for (var i = 0; i < actuators.Count; i++)
+                {
+                    var branchSizes = actuators[i].ActionSpec.BranchSizes;
+                    if (branchSizes != null)
+                    {
+                        Array.Copy(branchSizes, 0, combinedBranchSizes, start, branchSizes.Length);
+                        start += branchSizes.Length;
+                    }
+                }
+            }
+
+            return new ActionSpec(numContinuousActions, numDiscreteActions, combinedBranchSizes);
+        }
+
+        /// <summary>
+        /// Returns an ActionSpec representing the concatenation of all IActuator's ActionSpecs
+        /// </summary>
+        /// <returns></returns>
+        public ActionSpec GetCombinedActionSpec()
+        {
+            ReadyActuatorsForExecution();
+            return m_CombinedActionSpec;
         }
 
         /// <summary>

com.unity.ml-agents/Runtime/Actuators/IActionReceiver.cs

         /// <seealso cref="IActionReceiver.OnActionReceived"/>
         void WriteDiscreteActionMask(IDiscreteActionMask actionMask);
     }
+
+    /// <summary>
+    /// Helper methods to be shared by all classes that implement <see cref="IActionReceiver"/>.
+    /// </summary>
+    public static class ActionReceiverExtensions
+    {
+        /// <summary>
+        /// Returns the number of discrete branches + the number of continuous actions.
+        /// </summary>
+        /// <param name="actionReceiver"></param>
+        /// <returns></returns>
+        public static int TotalNumberOfActions(this IActionReceiver actionReceiver)
+        {
+            return actionReceiver.ActionSpec.NumContinuousActions + actionReceiver.ActionSpec.NumDiscreteActions;
+        }
+    }
 }

com.unity.ml-agents/Runtime/Actuators/IActuator.cs

     /// </summary>
     public interface IActuator : IActionReceiver
     {
-        int TotalNumberOfActions { get; }
-
         /// <summary>
         /// Gets the name of this IActuator which will be used to sort it.
         /// </summary>

com.unity.ml-agents/Runtime/Actuators/VectorActuator.cs

             Name = name + suffix;
         }
 
+        /// <inheritdoc />
+        /// <inheritdoc />
         public void OnActionReceived(ActionBuffers actionBuffers)
         {
             ActionBuffers = actionBuffers;
+        /// <inheritdoc />
         public void WriteDiscreteActionMask(IDiscreteActionMask actionMask)
         {
             m_ActionReceiver.WriteDiscreteActionMask(actionMask);
-        /// Returns the number of discrete branches + the number of continuous actions.
-        /// </summary>
-        public int TotalNumberOfActions => ActionSpec.NumContinuousActions +
-        ActionSpec.NumDiscreteActions;
-
-        /// <summary>
+        /// <inheritdoc />
         public string Name { get; }
     }
 }

com.unity.ml-agents/Runtime/Agent.cs

             Academy.Instance.DecideAction += DecideAction;
             Academy.Instance.AgentAct += AgentStep;
             Academy.Instance.AgentForceReset += _AgentReset;
-            m_Brain = m_PolicyFactory.GeneratePolicy(Heuristic);
+
+            using (TimerStack.Instance.Scoped("InitializeActuators"))
+            {
+                InitializeActuators();
+            }
+
+            m_Brain = m_PolicyFactory.GeneratePolicy(m_ActuatorManager.GetCombinedActionSpec(), Heuristic);
             ResetData();
             Initialize();
 
-            }
-
-            using (TimerStack.Instance.Scoped("InitializeActuators"))
-            {
-                InitializeActuators();
             }
 
             m_Info.storedVectorActions = new float[m_ActuatorManager.TotalNumberOfActions];
                 return;
             }
             m_Brain?.Dispose();
-            m_Brain = m_PolicyFactory.GeneratePolicy(Heuristic);
+            m_Brain = m_PolicyFactory.GeneratePolicy(m_ActuatorManager.GetCombinedActionSpec(), Heuristic);
         }
 
         /// <summary>
             }
 
             // Support legacy OnActionReceived
+            // TODO don't set this up if the sizes are 0?
-            m_LegacyActionCache = new float[m_VectorActuator.TotalNumberOfActions];
+            m_LegacyActionCache = new float[m_VectorActuator.TotalNumberOfActions()];
 
             m_ActuatorManager.Add(m_VectorActuator);
 
         /// actions. When using discrete actions, the agent will not perform the masked
         /// action.
         /// </summary>
-        /// <param name="actionMasker">
-        /// The action masker for the agent.
+        /// <param name="actionMask">
+        /// The action mask for the agent.
         /// </param>
         /// <remarks>
         /// When using Discrete Control, you can prevent the Agent from using a certain
         public virtual void OnEpisodeBegin() { }
 
         /// <summary>
-        /// Gets the last ActionBuffer for this agent.
+        /// Gets the most recent ActionBuffer for this agent.
+        /// <returns>The most recent ActionBuffer for this agent</returns>
         public ActionBuffers GetStoredActionBuffers()
         {
             return m_ActuatorManager.StoredActions;

com.unity.ml-agents/Runtime/Agent.deprecated.cs

 {
     public partial class Agent
     {
+        /// <summary>
+        /// Deprecated, use <see cref="WriteDiscreteActionMask"/> instead.
+        /// </summary>
+        /// <param name="actionMasker"></param>
-        /// This method passes in a float array that is to be populated with actions.  The actions
+        /// This method passes in a float array that is to be populated with actions.
         /// </summary>
         /// <param name="actionsOut"></param>
         public virtual void Heuristic(float[] actionsOut)
         }
 
+        /// <summary>
+        /// Deprecated, use <see cref="OnActionReceived(ActionBuffers)"/> instead.
+        /// </summary>
+        /// <param name="vectorAction"></param>
         public virtual void OnActionReceived(float[] vectorAction) { }
 
         /// <summary>
         {
             return m_Info.storedVectorActions;
         }
-
     }
 }

com.unity.ml-agents/Runtime/Communicator/GrpcExtensions.cs

 using Unity.MLAgents.CommunicatorObjects;
 using UnityEngine;
 using System.Runtime.CompilerServices;
+using Unity.MLAgents.Actuators;
 using Unity.MLAgents.Sensors;
 using Unity.MLAgents.Demonstrations;
 using Unity.MLAgents.Policies;
 
         #region BrainParameters
         /// <summary>
-        /// Converts a Brain into to a Protobuf BrainInfoProto so it can be sent
+        /// Converts a BrainParameters into to a BrainParametersProto so it can be sent.
         /// </summary>
         /// <returns>The BrainInfoProto generated.</returns>
         /// <param name="bp">The instance of BrainParameter to extend.</param>
             {
                 brainParametersProto.VectorActionDescriptions.AddRange(bp.VectorActionDescriptions);
             }
+            return brainParametersProto;
+        }
+
+        /// <summary>
+        /// Converts an ActionSpec into to a Protobuf BrainInfoProto so it can be sent.
+        /// </summary>
+        /// <returns>The BrainInfoProto generated.</returns>
+        /// <param name="actionSpec"> Description of the action spaces for the Agent.</param>
+        /// <param name="name">The name of the brain.</param>
+        /// <param name="isTraining">Whether or not the Brain is training.</param>
+        public static BrainParametersProto ToBrainParametersProto(this ActionSpec actionSpec, string name, bool isTraining)
+        {
+            actionSpec.CheckNotHybrid();
+
+            var brainParametersProto = new BrainParametersProto
+            {
+                BrainName = name,
+                IsTraining = isTraining
+            };
+            if (actionSpec.NumContinuousActions > 0)
+            {
+                brainParametersProto.VectorActionSize.Add(actionSpec.NumContinuousActions);
+                brainParametersProto.VectorActionSpaceType = SpaceTypeProto.Continuous;
+            }
+            else if (actionSpec.NumDiscreteActions > 0)
+            {
+                brainParametersProto.VectorActionSize.AddRange(actionSpec.BranchSizes);
+                brainParametersProto.VectorActionSpaceType = SpaceTypeProto.Discrete;
+            }
+
+            // TODO handle ActionDescriptions?
             return brainParametersProto;
         }
 

com.unity.ml-agents/Runtime/Communicator/ICommunicator.cs

 using System;
 using System.Collections.Generic;
+using Unity.MLAgents.Actuators;
 using Unity.MLAgents.Policies;
 using Unity.MLAgents.Sensors;
 
         /// Registers a new Brain to the Communicator.
         /// </summary>
         /// <param name="name">The name or key uniquely identifying the Brain.</param>
-        /// <param name="brainParameters">The Parameters for the Brain being registered.</param>
-        void SubscribeBrain(string name, BrainParameters brainParameters);
+        /// <param name="actionSpec"> Description of the action spaces for the Agent.</param>
+        void SubscribeBrain(string name, ActionSpec actionSpec);
 
         /// <summary>
         /// Sends the observations of one Agent.

com.unity.ml-agents/Runtime/Communicator/RpcCommunicator.cs

 using System.Collections.Generic;
 using System.Linq;
 using UnityEngine;
+using Unity.MLAgents.Actuators;
 using Unity.MLAgents.CommunicatorObjects;
 using Unity.MLAgents.Sensors;
 using Unity.MLAgents.Policies;
 
         // Brains that we have sent over the communicator with agents.
         HashSet<string> m_SentBrainKeys = new HashSet<string>();
-        Dictionary<string, BrainParameters> m_UnsentBrainKeys = new Dictionary<string, BrainParameters>();
+        Dictionary<string, ActionSpec> m_UnsentBrainKeys = new Dictionary<string, ActionSpec>();
 
 
 #if UNITY_EDITOR || UNITY_STANDALONE_WIN || UNITY_STANDALONE_OSX || UNITY_STANDALONE_LINUX
         /// Adds the brain to the list of brains which will be sending information to External.
         /// </summary>
         /// <param name="brainKey">Brain key.</param>
-        /// <param name="brainParameters">Brain parameters needed to send to the trainer.</param>
-        public void SubscribeBrain(string brainKey, BrainParameters brainParameters)
+        /// <param name="actionSpec"> Description of the action spaces for the Agent.</param>
+        public void SubscribeBrain(string brainKey, ActionSpec actionSpec)
         {
             if (m_BehaviorNames.Contains(brainKey))
             {
                 new UnityRLOutputProto.Types.ListAgentInfoProto()
             );
 
-            CacheBrainParameters(brainKey, brainParameters);
+            CacheActionSpec(brainKey, actionSpec);
         }
 
         void UpdateEnvironmentWithInput(UnityRLInputProto rlInput)
             message.RlOutput.SideChannel = ByteString.CopyFrom(messageAggregated);
 
             var input = Exchange(message);
-            UpdateSentBrainParameters(tempUnityRlInitializationOutput);
+            UpdateSentActionSpec(tempUnityRlInitializationOutput);
 
             foreach (var k in m_CurrentUnityRlOutput.AgentInfos.Keys)
             {
             };
         }
 
-        void CacheBrainParameters(string behaviorName, BrainParameters brainParameters)
+        void CacheActionSpec(string behaviorName, ActionSpec actionSpec)
         {
             if (m_SentBrainKeys.Contains(behaviorName))
             {
-            // TODO We should check that if m_unsentBrainKeys has brainKey, it equals brainParameters
-            m_UnsentBrainKeys[behaviorName] = brainParameters;
+            // TODO We should check that if m_unsentBrainKeys has brainKey, it equals actionSpec
+            m_UnsentBrainKeys[behaviorName] = actionSpec;
         }
 
         UnityRLInitializationOutputProto GetTempUnityRlInitializationOutput()
                 {
                     if (m_CurrentUnityRlOutput.AgentInfos[behaviorName].CalculateSize() > 0)
                     {
-                        // Only send the BrainParameters if there is a non empty list of
+                        // Only send the actionSpec if there is a non empty list of
-                        // observation when receiving the BrainParameters
+                        // observation when receiving the ActionSpec
-                        var brainParameters = m_UnsentBrainKeys[behaviorName];
-                        output.BrainParameters.Add(brainParameters.ToProto(behaviorName, true));
+                        var actionSpec = m_UnsentBrainKeys[behaviorName];
+                        output.BrainParameters.Add(actionSpec.ToBrainParametersProto(behaviorName, true));
                     }
                 }
             }
 
-        void UpdateSentBrainParameters(UnityRLInitializationOutputProto output)
+        void UpdateSentActionSpec(UnityRLInitializationOutputProto output)
         {
             if (output == null)
             {

com.unity.ml-agents/Runtime/Demonstrations/DemonstrationRecorder.cs

         public bool Record;
 
         /// <summary>
+        /// Number of steps to record. The editor will stop playing when it reaches this threshold.
+        /// Set to zero to record indefinitely.
+        /// </summary>
+        [Tooltip("Number of steps to record. The editor will stop playing when it reaches this threshold. " +
+                 "Set to zero to record indefinitely.")]
+        public int NumStepsToRecord = 0;
+
+        /// <summary>
         /// Base demonstration file name. If multiple files are saved, the additional filenames
         /// will have a sequence of unique numbers appended.
         /// </summary>
             if (Record)
             {
                 LazyInitialize();
+            }
+
+            if (NumStepsToRecord > 0 && m_DemoWriter.NumSteps >= NumStepsToRecord)
+            {
+                Application.Quit(0);
+#if UNITY_EDITOR
+                UnityEditor.EditorApplication.isPlaying = false;
+#endif
             }
         }
 

com.unity.ml-agents/Runtime/Demonstrations/DemonstrationWriter.cs

         }
 
         /// <summary>
+        /// Number of steps written so far.
+        /// </summary>
+        internal int NumSteps
+        {
+            get { return m_MetaData.numberSteps; }
+        }
+
+        /// <summary>
         /// Writes the initial data to the stream.
         /// </summary>
         /// <param name="demonstrationName">Base name of the demonstration file(s).</param>

com.unity.ml-agents/Runtime/DiscreteActionMasker.cs

             m_Delegate.WriteMask(branch, actionIndices);
         }
 
+        /// <inheritdoc />
+        /// <inheritdoc />
+        /// <inheritdoc />
         public void ResetMask()
         {
             m_Delegate.ResetMask();

com.unity.ml-agents/Runtime/Inference/BarracudaModelParamLoader.cs

 using System.Collections.Generic;
 using System.Linq;
 using Unity.Barracuda;
+using Unity.MLAgents.Actuators;
 using Unity.MLAgents.Sensors;
 using Unity.MLAgents.Policies;
 
         /// The BrainParameters that are used verify the compatibility with the InferenceEngine
         /// </param>
         /// <param name="sensorComponents">Attached sensor components</param>
+        /// <param name="actuatorComponents">Attached actuator components</param>
-            SensorComponent[] sensorComponents, int observableAttributeTotalSize = 0,
+            SensorComponent[] sensorComponents, ActuatorComponent[] actuatorComponents,
+            int observableAttributeTotalSize = 0,
             BehaviorType behaviorType = BehaviorType.Default)
         {
             List<string> failedModelChecks = new List<string>();
                 return failedModelChecks;
             }
 
+            var modelDiscreteActionSize = isContinuous == ModelActionType.Discrete ? actionSize : 0;
+            var modelContinuousActionSize = isContinuous == ModelActionType.Continuous ? actionSize : 0;
+
             failedModelChecks.AddRange(
                 CheckIntScalarPresenceHelper(new Dictionary<string, int>()
                 {
                 CheckInputTensorShape(model, brainParameters, sensorComponents, observableAttributeTotalSize)
             );
             failedModelChecks.AddRange(
-                CheckOutputTensorShape(model, brainParameters, isContinuous, actionSize)
+                CheckOutputTensorShape(model, brainParameters, actuatorComponents, isContinuous, modelContinuousActionSize, modelDiscreteActionSize)
             );
             return failedModelChecks;
         }
         /// <param name="brainParameters">
         /// The BrainParameters that are used verify the compatibility with the InferenceEngine
         /// </param>
+        /// <param name="actuatorComponents">Array of attached actuator components.</param>
-        /// <param name="modelActionSize">
-        /// The size of the action output that is expected by the model.
+        /// <param name="modelContinuousActionSize">
+        /// The size of the continuous action output that is expected by the model.
+        /// </param>
+        /// <param name="modelSumDiscreteBranchSizes">
+        /// The size of the discrete action output that is expected by the model.
         /// </param>
         /// <returns>
         /// A IEnumerable of string corresponding to the incompatible shapes between model
             Model model,
             BrainParameters brainParameters,
+            ActuatorComponent[] actuatorComponents,
-            int modelActionSize)
+            int modelContinuousActionSize, int modelSumDiscreteBranchSizes)
         {
             var failedModelChecks = new List<string>();
             if (isContinuous == ModelActionType.Unknown)
                     "suggest Continuous Control.");
                 return failedModelChecks;
             }
-            var tensorTester = new Dictionary<string, Func<BrainParameters, TensorShape?, int, string>>();
-            if (brainParameters.VectorActionSpaceType == SpaceType.Continuous)
+            var tensorTester = new Dictionary<string, Func<BrainParameters, ActuatorComponent[], TensorShape?, int, int, string>>();
+
+            // This will need to change a bit for hybrid action spaces.
+            if (isContinuous == ModelActionType.Continuous)
             {
                 tensorTester[TensorNames.ActionOutput] = CheckContinuousActionOutputShape;
             }
             }
+
-                    Func<BrainParameters, TensorShape?, int, string> tester = tensorTester[name];
-                    var error = tester.Invoke(brainParameters, model.GetShapeByName(name), modelActionSize);
+                    var tester = tensorTester[name];
+                    var error = tester.Invoke(brainParameters, actuatorComponents, model.GetShapeByName(name), modelContinuousActionSize, modelSumDiscreteBranchSizes);
                     if (error != null)
                     {
                         failedModelChecks.Add(error);
         /// <param name="brainParameters">
         /// The BrainParameters that are used verify the compatibility with the InferenceEngine
         /// </param>
+        /// <param name="actuatorComponents">Array of attached actuator components.</param>
-        /// <param name="modelActionSize">
-        /// The size of the action output that is expected by the model.
+        /// <param name="modelContinuousActionSize">
+        /// The size of the continuous action output that is expected by the model.
+        /// </param>
+        /// <param name="modelSumDiscreteBranchSizes">
+        /// The size of the discrete action output that is expected by the model.
         /// </param>
         /// <returns>
         /// If the Check failed, returns a string containing information about why the
-            BrainParameters brainParameters, TensorShape? shape, int modelActionSize)
+            BrainParameters brainParameters, ActuatorComponent[] actuatorComponents, TensorShape? shape, int modelContinuousActionSize, int modelSumDiscreteBranchSizes)
-            var bpActionSize = brainParameters.VectorActionSize.Sum();
-            if (modelActionSize != bpActionSize)
+            var sumOfDiscreteBranchSizes = 0;
+            if (brainParameters.VectorActionSpaceType == SpaceType.Discrete)
-                return "Action Size of the model does not match. The BrainParameters expect " +
-                    $"{bpActionSize} but the model contains {modelActionSize}.";
+                sumOfDiscreteBranchSizes += brainParameters.VectorActionSize.Sum();
+            }
+
+            foreach (var actuatorComponent in actuatorComponents)
+            {
+                var actionSpec = actuatorComponent.ActionSpec;
+                sumOfDiscreteBranchSizes += actionSpec.SumOfDiscreteBranchSizes;
+            }
+
+            if (modelSumDiscreteBranchSizes != sumOfDiscreteBranchSizes)
+            {
+                return "Discrete Action Size of the model does not match. The BrainParameters expect " +
+                    $"{sumOfDiscreteBranchSizes} but the model contains {modelSumDiscreteBranchSizes}.";
             }
             return null;
         }
         /// <param name="brainParameters">
         /// The BrainParameters that are used verify the compatibility with the InferenceEngine
         /// </param>
+        /// <param name="actuatorComponents">Array of attached actuator components.</param>
-        /// <param name="modelActionSize">
-        /// The size of the action output that is expected by the model.
+        /// <param name="modelContinuousActionSize">
+        /// The size of the continuous action output that is expected by the model.
+        /// </param>
+        /// <param name="modelSumDiscreteBranchSizes">
+        /// The size of the discrete action output that is expected by the model.
-            BrainParameters brainParameters, TensorShape? shape, int modelActionSize)
+            BrainParameters brainParameters, ActuatorComponent[] actuatorComponents, TensorShape? shape, int modelContinuousActionSize, int modelSumDiscreteBranchSizes)
-            var bpActionSize = brainParameters.VectorActionSize[0];
-            if (modelActionSize != bpActionSize)
+            var numContinuousActions = 0;
+            if (brainParameters.VectorActionSpaceType == SpaceType.Continuous)
-                return "Action Size of the model does not match. The BrainParameters expect " +
-                    $"{bpActionSize} but the model contains {modelActionSize}.";
+                numContinuousActions += brainParameters.NumActions;
+            }
+
+            foreach (var actuatorComponent in actuatorComponents)
+            {
+                var actionSpec = actuatorComponent.ActionSpec;
+                numContinuousActions += actionSpec.NumContinuousActions;
+            }
+
+            if (modelContinuousActionSize != numContinuousActions)
+            {
+                return "Continuous Action Size of the model does not match. The BrainParameters and ActuatorComponents expect " +
+                    $"{numContinuousActions} but the model contains {modelContinuousActionSize}.";
             }
             return null;
         }

com.unity.ml-agents/Runtime/Inference/ModelRunner.cs

 using System.Collections.Generic;
 using Unity.Barracuda;
 using UnityEngine.Profiling;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Policies;
-using Unity.MLAgents.Policies;
 
 namespace Unity.MLAgents.Inference
 {
         /// the agents
         /// </summary>
         /// <param name="model"> The Barracuda model to load </param>
-        /// <param name="brainParameters"> The parameters of the Brain used to generate the
-        /// placeholder tensors </param>
+        /// <param name="actionSpec"> Description of the action spaces for the Agent.</param>
         /// <param name="inferenceDevice"> Inference execution device. CPU is the fastest
         /// option for most of ML Agents models. </param>
         /// <param name="seed"> The seed that will be used to initialize the RandomNormal
         public ModelRunner(
             NNModel model,
-            BrainParameters brainParameters,
+            ActionSpec actionSpec,
             InferenceDevice inferenceDevice = InferenceDevice.CPU,
             int seed = 0)
         {
             m_TensorGenerator = new TensorGenerator(
                 seed, m_TensorAllocator, m_Memories, barracudaModel);
             m_TensorApplier = new TensorApplier(
-                brainParameters, seed, m_TensorAllocator, m_Memories, barracudaModel);
+                actionSpec, seed, m_TensorAllocator, m_Memories, barracudaModel);
         }
 
         static Dictionary<string, Tensor> PrepareBarracudaInputs(IEnumerable<TensorProxy> infInputs)

com.unity.ml-agents/Runtime/Inference/TensorApplier.cs

 using System.Collections.Generic;
 using Unity.Barracuda;
+using Unity.MLAgents.Actuators;
+
 
 namespace Unity.MLAgents.Inference
 {
         /// <summary>
         /// Returns a new TensorAppliers object.
         /// </summary>
-        /// <param name="bp"> The BrainParameters used to determine what Appliers will be
-        /// used</param>
+        /// <param name="actionSpec"> Description of the action spaces for the Agent.</param>
-            BrainParameters bp,
+            ActionSpec actionSpec,
-            if (bp.VectorActionSpaceType == SpaceType.Continuous)
+            actionSpec.CheckNotHybrid();
+
+            if (actionSpec.NumContinuousActions > 0)
             {
                 m_Dict[TensorNames.ActionOutput] = new ContinuousActionOutputApplier();
             }
-                    new DiscreteActionOutputApplier(bp.VectorActionSize, seed, allocator);
+                    new DiscreteActionOutputApplier(actionSpec.BranchSizes, seed, allocator);
             }
             m_Dict[TensorNames.RecurrentOutput] = new MemoryOutputApplier(memories);
 

com.unity.ml-agents/Runtime/Policies/BarracudaPolicy.cs

 
         /// <inheritdoc />
         public BarracudaPolicy(
-            BrainParameters brainParameters,
+            ActionSpec actionSpec,
-            var modelRunner = Academy.Instance.GetOrCreateModelRunner(model, brainParameters, inferenceDevice);
+            var modelRunner = Academy.Instance.GetOrCreateModelRunner(model, actionSpec, inferenceDevice);
-            m_SpaceType = brainParameters.VectorActionSpaceType;
+            actionSpec.CheckNotHybrid();
+            m_SpaceType = actionSpec.NumContinuousActions > 0 ? SpaceType.Continuous : SpaceType.Discrete;
         }
 
         /// <inheritdoc />

com.unity.ml-agents/Runtime/Policies/BehaviorParameters.cs

 using System;
 using UnityEngine;
 using UnityEngine.Serialization;
+using Unity.MLAgents.Actuators;
 using Unity.MLAgents.Sensors.Reflection;
 
 namespace Unity.MLAgents.Policies
             get { return m_BehaviorName + "?team=" + TeamId; }
         }
 
-        internal IPolicy GeneratePolicy(HeuristicPolicy.ActionGenerator heuristic)
+        internal IPolicy GeneratePolicy(ActionSpec actionSpec, HeuristicPolicy.ActionGenerator heuristic)
-                    return GenerateHeuristicPolicy(heuristic);
+                    return new HeuristicPolicy(heuristic, actionSpec);
                 case BehaviorType.InferenceOnly:
                     {
                         if (m_Model == null)
                                 "Either assign a model, or change to a different Behavior Type."
                             );
                         }
-                        return new BarracudaPolicy(m_BrainParameters, m_Model, m_InferenceDevice);
+                        return new BarracudaPolicy(actionSpec, m_Model, m_InferenceDevice);
-                        return new RemotePolicy(m_BrainParameters, FullyQualifiedBehaviorName);
+                        return new RemotePolicy(actionSpec, FullyQualifiedBehaviorName);
-                        return new BarracudaPolicy(m_BrainParameters, m_Model, m_InferenceDevice);
+                        return new BarracudaPolicy(actionSpec, m_Model, m_InferenceDevice);
-                        return GenerateHeuristicPolicy(heuristic);
+                        return new HeuristicPolicy(heuristic, actionSpec);
-                    return GenerateHeuristicPolicy(heuristic);
-            }
-        }
-
-        internal IPolicy GenerateHeuristicPolicy(HeuristicPolicy.ActionGenerator heuristic)
-        {
-            var numContinuousActions = 0;
-            var numDiscreteActions = 0;
-            if (m_BrainParameters.VectorActionSpaceType == SpaceType.Continuous)
-            {
-                numContinuousActions = m_BrainParameters.NumActions;
+                    return new HeuristicPolicy(heuristic, actionSpec);
-            else if (m_BrainParameters.VectorActionSpaceType == SpaceType.Discrete)
-            {
-                numDiscreteActions = m_BrainParameters.NumActions;
-            }
-
-            return new HeuristicPolicy(heuristic, numContinuousActions, numDiscreteActions);
         }
 
         internal void UpdateAgentPolicy()

com.unity.ml-agents/Runtime/Policies/HeuristicPolicy.cs

 
 
         /// <inheritdoc />
-        public HeuristicPolicy(ActionGenerator heuristic, int numContinuousActions, int numDiscreteActions)
+        public HeuristicPolicy(ActionGenerator heuristic, ActionSpec actionSpec)
+            var numContinuousActions = actionSpec.NumContinuousActions;
+            var numDiscreteActions = actionSpec.NumDiscreteActions;
             var continuousDecision = new ActionSegment<float>(new float[numContinuousActions], 0, numContinuousActions);
             var discreteDecision = new ActionSegment<int>(new int[numDiscreteActions], 0, numDiscreteActions);
             m_ActionBuffers = new ActionBuffers(continuousDecision, discreteDecision);

com.unity.ml-agents/Runtime/Policies/RemotePolicy.cs

 
         /// <inheritdoc />
         public RemotePolicy(
-            BrainParameters brainParameters,
+            ActionSpec actionSpec,
-            m_SpaceType = brainParameters.VectorActionSpaceType;
-            m_Communicator.SubscribeBrain(m_FullyQualifiedBehaviorName, brainParameters);
+            m_Communicator.SubscribeBrain(m_FullyQualifiedBehaviorName, actionSpec);
+
+            actionSpec.CheckNotHybrid();
+            m_SpaceType = actionSpec.NumContinuousActions > 0 ? SpaceType.Continuous : SpaceType.Discrete;
         }
 
         /// <inheritdoc />
         {
             m_Communicator?.DecideBatch();
             var actions = m_Communicator?.GetActions(m_FullyQualifiedBehaviorName, m_AgentId);
+            // TODO figure out how to handle this with multiple space types.
             if (m_SpaceType == SpaceType.Continuous)
             {
                 m_LastActionBuffer = new ActionBuffers(actions, Array.Empty<int>());

com.unity.ml-agents/Tests/Editor/Actuators/ActuatorManagerTests.cs

             var actuator2 = new TestActuator(ActionSpec.MakeContinuous(3), "actuator2");
             manager.Add(actuator1);
             manager.Add(actuator2);
-            manager.ReadyActuatorsForExecution(new[] { actuator1, actuator2 }, 3, 10, 4);
+            manager.ReadyActuatorsForExecution(new[] { actuator1, actuator2 }, 3, 10, 4);
         }
 
         [Test]

com.unity.ml-agents/Tests/Editor/Actuators/TestActuator.cs

         public TestActuator(ActionSpec actuatorSpace, string name)
         {
             ActionSpec = actuatorSpace;
-            TotalNumberOfActions = actuatorSpace.NumContinuousActions +
-                actuatorSpace.NumDiscreteActions;
+
             Name = name;
         }
 
             }
         }
 
-        public int TotalNumberOfActions { get; }
         public ActionSpec ActionSpec { get; }
 
         public string Name { get; }

com.unity.ml-agents/Tests/Editor/BehaviorParameterTests.cs

             var gameObj = new GameObject();
             var bp = gameObj.AddComponent<BehaviorParameters>();
             bp.BehaviorType = BehaviorType.InferenceOnly;
+            var actionSpec = new ActionSpec();
-                bp.GeneratePolicy(DummyHeuristic);
+                bp.GeneratePolicy(actionSpec, DummyHeuristic);
             });
         }
     }

com.unity.ml-agents/Tests/Editor/Communicator/GrpcExtensionsTests.cs

 using UnityEngine;
 using Unity.MLAgents.Policies;
 using Unity.MLAgents.Demonstrations;
-using Unity.MLAgents.Sensors;
+using Unity.MLAgents.Actuators;
 
 namespace Unity.MLAgents.Tests
 {
             // Should be able to convert a default instance to proto.
             var brain = new BrainParameters();
             brain.ToProto("foo", false);
+        }
+
+        [Test]
+        public void TestDefaultActionSpecToProto()
+        {
+            // Should be able to convert a default instance to proto.
+            var actionSpec = new ActionSpec();
+            actionSpec.ToBrainParametersProto("foo", false);
+
+            // Continuous
+            actionSpec = ActionSpec.MakeContinuous(3);
+            actionSpec.ToBrainParametersProto("foo", false);
+
+            // Discrete
+            actionSpec = ActionSpec.MakeDiscrete(1, 2, 3);
+            actionSpec.ToBrainParametersProto("foo", false);
         }
 
         [Test]

com.unity.ml-agents/Tests/Editor/EditModeTestInternalBrainTensorApplier.cs

 using System.Collections.Generic;
 using NUnit.Framework;
 using Unity.Barracuda;
+using Unity.MLAgents.Actuators;
 using Unity.MLAgents.Inference;
 using Unity.MLAgents.Policies;
 
         [Test]
         public void Construction()
         {
-            var bp = new BrainParameters();
+            var actionSpec = new ActionSpec();
-            var tensorGenerator = new TensorApplier(bp, 0, alloc, mem);
+            var tensorGenerator = new TensorApplier(actionSpec, 0, alloc, mem);
             Assert.IsNotNull(tensorGenerator);
             alloc.Dispose();
         }

com.unity.ml-agents/Tests/Editor/ModelRunnerTest.cs

 using UnityEngine;
 using UnityEditor;
 using Unity.Barracuda;
+using Unity.MLAgents.Actuators;
 using Unity.MLAgents.Inference;
 using Unity.MLAgents.Sensors;
 using Unity.MLAgents.Policies;
         Test3DSensorComponent sensor_21_20_3;
         Test3DSensorComponent sensor_20_22_3;
 
-        BrainParameters GetContinuous2vis8vec2actionBrainParameters()
+        ActionSpec GetContinuous2vis8vec2actionActionSpec()
-            var validBrainParameters = new BrainParameters();
-            validBrainParameters.VectorObservationSize = 8;
-            validBrainParameters.VectorActionSize = new[] { 2 };
-            validBrainParameters.NumStackedVectorObservations = 1;
-            validBrainParameters.VectorActionSpaceType = SpaceType.Continuous;
-            return validBrainParameters;
+            return ActionSpec.MakeContinuous(2);
-        BrainParameters GetDiscrete1vis0vec_2_3action_recurrModelBrainParameters()
+        ActionSpec GetDiscrete1vis0vec_2_3action_recurrModelActionSpec()
-            var validBrainParameters = new BrainParameters();
-            validBrainParameters.VectorObservationSize = 0;
-            validBrainParameters.VectorActionSize = new[] { 2, 3 };
-            validBrainParameters.NumStackedVectorObservations = 1;
-            validBrainParameters.VectorActionSpaceType = SpaceType.Discrete;
-            return validBrainParameters;
+            return ActionSpec.MakeDiscrete(2, 3);
         }
 
         [SetUp]
         [Test]
         public void TestCreation()
         {
-            var modelRunner = new ModelRunner(continuous2vis8vec2actionModel, GetContinuous2vis8vec2actionBrainParameters());
+            var modelRunner = new ModelRunner(continuous2vis8vec2actionModel, GetContinuous2vis8vec2actionActionSpec());
-            modelRunner = new ModelRunner(discrete1vis0vec_2_3action_recurrModel, GetDiscrete1vis0vec_2_3action_recurrModelBrainParameters());
+            modelRunner = new ModelRunner(discrete1vis0vec_2_3action_recurrModel, GetDiscrete1vis0vec_2_3action_recurrModelActionSpec());
             modelRunner.Dispose();
         }
 
-            var modelRunner = new ModelRunner(continuous2vis8vec2actionModel, GetContinuous2vis8vec2actionBrainParameters(), InferenceDevice.CPU);
+            var modelRunner = new ModelRunner(continuous2vis8vec2actionModel, GetContinuous2vis8vec2actionActionSpec(), InferenceDevice.CPU);
             Assert.True(modelRunner.HasModel(continuous2vis8vec2actionModel, InferenceDevice.CPU));