Merge branch 'main' into develop-coma2-trainer

.yamato/python-ll-api-test.yml

       python ml-agents/tests/yamato/scripts/run_llapi.py
       python ml-agents/tests/yamato/scripts/run_llapi.py --env=artifacts/testPlayer-Basic
       python ml-agents/tests/yamato/scripts/run_llapi.py --env=artifacts/testPlayer-WallJump
-      python ml-agents/tests/yamato/scripts/run_llapi.py --env=artifacts/testPlayer-Bouncer
+      python ml-agents/tests/yamato/scripts/run_llapi.py --env=artifacts/testPlayer-Match3
   dependencies:
     - .yamato/standalone-build-test.yml#test_linux_standalone_{{ editor.version }}
   triggers:

.yamato/standalone-build-test.yml

     - unity-downloader-cli -u {{ editor.version }} -c editor --wait --fast
     - python3 -u -m ml-agents.tests.yamato.standalone_build_tests --build-target=linux
     - python3 -u -m ml-agents.tests.yamato.standalone_build_tests --build-target=linux --scene=Assets/ML-Agents/Examples/Basic/Scenes/Basic.unity
-    - python3 -u -m ml-agents.tests.yamato.standalone_build_tests --build-target=linux --scene=Assets/ML-Agents/Examples/Bouncer/Scenes/Bouncer.unity
+    - python3 -u -m ml-agents.tests.yamato.standalone_build_tests --build-target=linux --scene=Assets/ML-Agents/Examples/Match3/Scenes/Match3.unity
     - python3 -u -m ml-agents.tests.yamato.standalone_build_tests --build-target=linux --scene=Assets/ML-Agents/Examples/WallJump/Scenes/WallJump.unity
     - python3 -u -m ml-agents.tests.yamato.standalone_build_tests --build-target=linux --scene=Assets/ML-Agents/TestScenes/TestCompressedGrid/TestGridCompressed.unity
     - python3 -u -m ml-agents.tests.yamato.standalone_build_tests --build-target=linux --scene=Assets/ML-Agents/TestScenes/TestCompressedTexture/TestTextureCompressed.unity

DevProject/Packages/packages-lock.json

       "url": "https://artifactory.prd.cds.internal.unity3d.com/artifactory/api/npm/upm-candidates"
     },
     "com.unity.barracuda": {
-      "version": "1.3.0-preview",
+      "version": "1.3.1-preview",
       "depth": 1,
       "source": "registry",
       "dependencies": {
       "depth": 0,
       "source": "local",
       "dependencies": {
-        "com.unity.barracuda": "1.3.0-preview",
+        "com.unity.barracuda": "1.3.1-preview",
         "com.unity.modules.imageconversion": "1.0.0",
         "com.unity.modules.jsonserialize": "1.0.0",
         "com.unity.modules.physics": "1.0.0",
       "depth": 0,
       "source": "local",
       "dependencies": {
-        "com.unity.ml-agents": "1.7.2-preview"
+        "com.unity.ml-agents": "1.8.0-preview"
       }
     },
     "com.unity.multiplayer-hlapi": {

Project/Assets/ML-Agents/Examples/Crawler/Demos/ExpertCrawler.demo.meta

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

 [RequireComponent(typeof(JointDriveController))] // Required to set joint forces
 public class CrawlerAgent : Agent
 {
-    //The type of crawler behavior we want to use.
-    //This setting will determine how the agent is set up during initialization.
-    public enum CrawlerAgentBehaviorType
-    {
-        CrawlerDynamic,
-        CrawlerDynamicVariableSpeed,
-        CrawlerStatic,
-        CrawlerStaticVariableSpeed
-    }
-
-    [Tooltip(
-        "VariableSpeed - The agent will sample random speed magnitudes while training.\n" +
-        "Dynamic - The agent will run towards a target that changes position.\n" +
-        "Static - The agent will run towards a static target. "
-    )]
-    public CrawlerAgentBehaviorType typeOfCrawler;
-
-    //Crawler Brains
-    //A different brain will be used depending on the CrawlerAgentBehaviorType selected
-    [Header("NN Models")] public NNModel crawlerDyModel;
-    public NNModel crawlerDyVSModel;
-    public NNModel crawlerStModel;
-    public NNModel crawlerStVSModel;
 
     [Header("Walk Speed")]
     [Range(0.1f, m_maxWalkingSpeed)]
         set { m_TargetWalkingSpeed = Mathf.Clamp(value, .1f, m_maxWalkingSpeed); }
     }
 
-    //Should the agent sample a new goal velocity each episode?
-    //If true, TargetWalkingSpeed will be randomly set between 0.1 and m_maxWalkingSpeed in OnEpisodeBegin()
-    //If false, the goal velocity will be m_maxWalkingSpeed
-    private bool m_RandomizeWalkSpeedEachEpisode;
-
-    [Header("Target To Walk Towards")] public Transform dynamicTargetPrefab; //Target prefab to use in Dynamic envs
-    public Transform staticTargetPrefab; //Target prefab to use in Static envs
+    [Header("Target To Walk Towards")]
+    public Transform TargetPrefab; //Target prefab to use in Dynamic envs
     private Transform m_Target; //Target the agent will walk towards during training.
 
     [Header("Body Parts")] [Space(10)] public Transform body;
 
     public override void Initialize()
     {
-        SetAgentType();
+        SpawnTarget(TargetPrefab, transform.position); //spawn target
 
         m_OrientationCube = GetComponentInChildren<OrientationCubeController>();
         m_DirectionIndicator = GetComponentInChildren<DirectionIndicator>();
     }
 
     /// <summary>
-    /// Set up the agent based on the typeOfCrawler
-    /// </summary>
-    void SetAgentType()
-    {
-        var behaviorParams = GetComponent<Unity.MLAgents.Policies.BehaviorParameters>();
-        switch (typeOfCrawler)
-        {
-            case CrawlerAgentBehaviorType.CrawlerDynamic:
-                {
-                    behaviorParams.BehaviorName = "CrawlerDynamic"; //set behavior name
-                    if (crawlerDyModel)
-                        behaviorParams.Model = crawlerDyModel; //assign the model
-                    m_RandomizeWalkSpeedEachEpisode = false; //do not randomize m_TargetWalkingSpeed during training
-                    SpawnTarget(dynamicTargetPrefab, transform.position); //spawn target
-                    break;
-                }
-            case CrawlerAgentBehaviorType.CrawlerDynamicVariableSpeed:
-                {
-                    behaviorParams.BehaviorName = "CrawlerDynamicVariableSpeed"; //set behavior name
-                    if (crawlerDyVSModel)
-                        behaviorParams.Model = crawlerDyVSModel; //assign the model
-                    m_RandomizeWalkSpeedEachEpisode = true; //randomize m_TargetWalkingSpeed during training
-                    SpawnTarget(dynamicTargetPrefab, transform.position); //spawn target
-                    break;
-                }
-            case CrawlerAgentBehaviorType.CrawlerStatic:
-                {
-                    behaviorParams.BehaviorName = "CrawlerStatic"; //set behavior name
-                    if (crawlerStModel)
-                        behaviorParams.Model = crawlerStModel; //assign the model
-                    m_RandomizeWalkSpeedEachEpisode = false; //do not randomize m_TargetWalkingSpeed during training
-                    SpawnTarget(staticTargetPrefab, transform.TransformPoint(new Vector3(0, 0, 1000))); //spawn target
-                    break;
-                }
-            case CrawlerAgentBehaviorType.CrawlerStaticVariableSpeed:
-                {
-                    behaviorParams.BehaviorName = "CrawlerStaticVariableSpeed"; //set behavior name
-                    if (crawlerStVSModel)
-                        behaviorParams.Model = crawlerStVSModel; //assign the model
-                    m_RandomizeWalkSpeedEachEpisode = true; //randomize m_TargetWalkingSpeed during training
-                    SpawnTarget(staticTargetPrefab, transform.TransformPoint(new Vector3(0, 0, 1000))); //spawn target
-                    break;
-                }
-        }
-    }
-
-    /// <summary>
     /// Loop over body parts and reset them to initial conditions.
     /// </summary>
     public override void OnEpisodeBegin()
         UpdateOrientationObjects();
 
         //Set our goal walking speed
-        TargetWalkingSpeed =
-            m_RandomizeWalkSpeedEachEpisode ? Random.Range(0.1f, m_maxWalkingSpeed) : TargetWalkingSpeed;
+        TargetWalkingSpeed = Random.Range(0.1f, m_maxWalkingSpeed);
     }
 
     /// <summary>

Project/Assets/ML-Agents/Examples/FoodCollector/Prefabs/FoodCollectorArea.prefab

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Project/Assets/ML-Agents/Examples/Sorter/Scripts/SorterAgent.cs

 
     int m_NumberOfTilesToSpawn;
     int m_MaxNumberOfTiles;
-    PushBlockSettings m_PushBlockSettings;
     Rigidbody m_AgentRb;
 
     // The BufferSensorComponent is the Sensor that allows the Agent to observe
         m_MaxNumberOfTiles = k_HighestTileValue;
         m_ResetParams = Academy.Instance.EnvironmentParameters;
         m_BufferSensor = GetComponent<BufferSensorComponent>();
-        m_PushBlockSettings = FindObjectOfType<PushBlockSettings>();
         m_AgentRb = GetComponent<Rigidbody>();
         m_StartingPos = transform.position;
     }
         }
 
         transform.Rotate(rotateDir, Time.deltaTime * 200f);
-        m_AgentRb.AddForce(dirToGo * m_PushBlockSettings.agentRunSpeed,
-            ForceMode.VelocityChange);
+        m_AgentRb.AddForce(dirToGo * 2, ForceMode.VelocityChange);
 
     }
 

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     VectorActionSpaceType: 1
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+  m_BehaviorName: Worm
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Project/Assets/ML-Agents/Examples/Worm/Scripts/WormAgent.cs

 [RequireComponent(typeof(JointDriveController))] // Required to set joint forces
 public class WormAgent : Agent
 {
-    //The type of agent behavior we want to use.
-    //This setting will determine how the agent is set up during initialization.
-    public enum WormAgentBehaviorType
-    {
-        WormDynamic,
-        WormStatic
-    }
-
-    [Tooltip(
-        "Dynamic - The agent will run towards a target that changes position.\n\n" +
-        "Static - The agent will run towards a static target. "
-    )]
-    public WormAgentBehaviorType typeOfWorm;
-
-    //Brains
-    //A different brain will be used depending on the CrawlerAgentBehaviorType selected
-    [Header("NN Models")] public NNModel wormDyModel;
-    public NNModel wormStModel;
-
-    [Header("Target Prefabs")] public Transform dynamicTargetPrefab; //Target prefab to use in Dynamic envs
-    public Transform staticTargetPrefab; //Target prefab to use in Static envs
+    [Header("Target Prefabs")] public Transform TargetPrefab; //Target prefab to use in Dynamic envs
     private Transform m_Target; //Target the agent will walk towards during training.
 
     [Header("Body Parts")] public Transform bodySegment0;
 
     public override void Initialize()
     {
-        SetAgentType();
-
+        SpawnTarget(TargetPrefab, transform.position); //spawn target
 
         m_StartingPos = bodySegment0.position;
         m_OrientationCube = GetComponentInChildren<OrientationCubeController>();
     void SpawnTarget(Transform prefab, Vector3 pos)
     {
         m_Target = Instantiate(prefab, pos, Quaternion.identity, transform);
-    }
-
-    /// <summary>
-    /// Set up the agent based on the type
-    /// </summary>
-    void SetAgentType()
-    {
-        var behaviorParams = GetComponent<Unity.MLAgents.Policies.BehaviorParameters>();
-        switch (typeOfWorm)
-        {
-            case WormAgentBehaviorType.WormDynamic:
-                {
-                    behaviorParams.BehaviorName = "WormDynamic"; //set behavior name
-                    if (wormDyModel)
-                        behaviorParams.Model = wormDyModel; //assign the brain
-                    SpawnTarget(dynamicTargetPrefab, transform.position); //spawn target
-                    break;
-                }
-            case WormAgentBehaviorType.WormStatic:
-                {
-                    behaviorParams.BehaviorName = "WormStatic"; //set behavior name
-                    if (wormStModel)
-                        behaviorParams.Model = wormStModel; //assign the brain
-                    SpawnTarget(staticTargetPrefab, transform.TransformPoint(new Vector3(0, 0, 1000))); //spawn target
-                    break;
-                }
-        }
     }
 
     /// <summary>

com.unity.ml-agents.extensions/Runtime/Input/Adaptors/ButtonInputActionAdaptor.cs

 using Unity.MLAgents.Actuators;
 using UnityEngine;
 using UnityEngine.InputSystem;
+using UnityEngine.InputSystem.Controls;
 using UnityEngine.InputSystem.LowLevel;
 
 namespace Unity.MLAgents.Extensions.Input
         }
 
         /// TODO again this might need to be more nuanced for things like continuous buttons.
-        /// <inheritdoc cref="IRLActionInputAdaptor.QueueInputEventForAction"/>
-        public void QueueInputEventForAction(InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers)
+        /// <inheritdoc cref="IRLActionInputAdaptor.WriteToInputEventForAction"/>
+        public void WriteToInputEventForAction(InputEventPtr eventPtr, InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers)
-            InputSystem.QueueDeltaStateEvent(control, (byte)val);
+            ((ButtonControl)control).WriteValueIntoEvent((float)val, eventPtr);
         }
 
         /// <inheritdoc cref="IRLActionInputAdaptor.WriteToHeuristic"/>>

com.unity.ml-agents.extensions/Runtime/Input/Adaptors/DoubleInputActionAdaptor.cs

 #if MLA_INPUT_SYSTEM && UNITY_2019_4_OR_NEWER
 using Unity.MLAgents.Actuators;
 using UnityEngine.InputSystem;
+using UnityEngine.InputSystem.Controls;
 using UnityEngine.InputSystem.LowLevel;
 
 namespace Unity.MLAgents.Extensions.Input
             return ActionSpec.MakeContinuous(1);
         }
 
-        /// <inheritdoc cref="IRLActionInputAdaptor.QueueInputEventForAction"/>
-        public void QueueInputEventForAction(InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers)
+        /// <inheritdoc cref="IRLActionInputAdaptor.WriteToInputEventForAction"/>
+        public void WriteToInputEventForAction(InputEventPtr eventPtr, InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers)
-            InputSystem.QueueDeltaStateEvent(control,(double)val);
+            ((DoubleControl)control).WriteValueIntoEvent((double)val, eventPtr);
         }
 
         /// <inheritdoc cref="IRLActionInputAdaptor.WriteToHeuristic"/>

com.unity.ml-agents.extensions/Runtime/Input/Adaptors/FloatInputActionAdaptor.cs

             return ActionSpec.MakeContinuous(1);
         }
 
-        /// <inheritdoc cref="IRLActionInputAdaptor.QueueInputEventForAction"/>
-        public void QueueInputEventForAction(InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers)
+        /// <inheritdoc cref="IRLActionInputAdaptor.WriteToInputEventForAction"/>
+        public void WriteToInputEventForAction(InputEventPtr eventPtr, InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers)
-            InputSystem.QueueDeltaStateEvent(control, val);
+            control.WriteValueIntoEvent(val, eventPtr);
         }
 
         /// <inheritdoc cref="IRLActionInputAdaptor.WriteToHeuristic"/>

com.unity.ml-agents.extensions/Runtime/Input/Adaptors/IntegerInputActionAdaptor.cs

             return ActionSpec.MakeDiscrete(2);
         }
 
-        /// <inheritdoc cref="IRLActionInputAdaptor.QueueInputEventForAction"/>
-        public void QueueInputEventForAction(InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers)
+        /// <inheritdoc cref="IRLActionInputAdaptor.WriteToInputEventForAction"/>
+        public void WriteToInputEventForAction(InputEventPtr eventPtr, InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers)
-            InputSystem.QueueDeltaStateEvent(control, val);
+            control.WriteValueIntoEvent(val, eventPtr);
         }
 
         /// <inheritdoc cref="IRLActionInputAdaptor.WriteToHeuristic"/>

com.unity.ml-agents.extensions/Runtime/Input/Adaptors/Vector2InputActionAdaptor.cs

 using Unity.MLAgents.Actuators;
 using UnityEngine;
 using UnityEngine.InputSystem;
+using UnityEngine.InputSystem.Controls;
 using UnityEngine.InputSystem.LowLevel;
 
 namespace Unity.MLAgents.Extensions.Input
             return ActionSpec.MakeContinuous(2);
         }
 
-        /// <inheritdoc cref="IRLActionInputAdaptor.QueueInputEventForAction"/>
-        public void QueueInputEventForAction(InputAction action,
+        /// <inheritdoc cref="IRLActionInputAdaptor.WriteToInputEventForAction"/>
+        public void WriteToInputEventForAction(InputEventPtr eventPtr, InputAction action,
             InputControl control,
             ActionSpec actionSpec,
             in ActionBuffers actionBuffers)
-            InputSystem.QueueDeltaStateEvent(control, new Vector2(x, y));
+            control.WriteValueIntoEvent(new Vector2(x, y), eventPtr);
         }
 
         /// <inheritdoc cref="IRLActionInputAdaptor.WriteToHeuristic"/>

com.unity.ml-agents.extensions/Runtime/Input/IRLActionInputAdaptor.cs

 using System;
 using Unity.MLAgents.Actuators;
 using UnityEngine.InputSystem;
+using UnityEngine.InputSystem.LowLevel;
 
 namespace Unity.MLAgents.Extensions.Input
 {
         /// <summary>
         /// Translates data from the <see cref="ActionBuffers"/> object to the <see cref="InputSystem"/>.
         /// </summary>
+        /// <param name="eventPtr">The Event pointer to write to.</param>
-        void QueueInputEventForAction(InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers);
+        void WriteToInputEventForAction(InputEventPtr eventPtr, InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers);
 
         /// <summary>
         /// Writes data from the <paramref name="action"/> to the <paramref name="actionBuffers"/>.

com.unity.ml-agents.extensions/Runtime/Input/InputActionActuator.cs

         readonly BehaviorParameters m_BehaviorParameters;
         readonly InputAction m_Action;
         readonly IRLActionInputAdaptor m_InputAdaptor;
+        InputActuatorEventContext m_InputActuatorEventContext;
         InputDevice m_Device;
         InputControl m_Control;
 
         ///     via the <see cref="IRLActionInputAdaptor"/>.</param>
         /// <param name="adaptor">The <see cref="IRLActionInputAdaptor"/> that will convert data between ML-Agents
         ///     and the <see cref="InputSystem"/>.</param>
+        /// <param name="inputActuatorEventContext">The object that will provide the event ptr to write to.</param>
-                                   IRLActionInputAdaptor adaptor)
+                                   IRLActionInputAdaptor adaptor,
+                                   InputActuatorEventContext inputActuatorEventContext)
+            m_InputActuatorEventContext = inputActuatorEventContext;
             ActionSpec = adaptor.GetActionSpecForInputAction(m_Action);
             m_Device = inputDevice;
             m_Control = m_Device?.GetChildControl(m_Action.name);
             Profiler.BeginSample("InputActionActuator.OnActionReceived");
             if (!m_BehaviorParameters.IsInHeuristicMode())
             {
-                m_InputAdaptor.QueueInputEventForAction(m_Action, m_Control, ActionSpec, actionBuffers);
+                using (m_InputActuatorEventContext.GetEventForFrame(out var eventPtr))
+                {
+                    m_InputAdaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, ActionSpec, actionBuffers);
+                }
+
             }
             Profiler.EndSample();
         }

com.unity.ml-agents.extensions/Runtime/Input/InputActuatorComponent.cs

 #if MLA_INPUT_SYSTEM && UNITY_2019_4_OR_NEWER
 using System;
 using System.Collections.Generic;
+using Unity.Collections;
 using Unity.MLAgents.Actuators;
 using Unity.MLAgents.Policies;
 using UnityEngine;
+using UnityEngine.InputSystem.LowLevel;
 using UnityEngine.InputSystem.Layouts;
 using UnityEngine.InputSystem.Utilities;
 #if UNITY_EDITOR
             get
             {
 #if UNITY_EDITOR
-                FindNeededComponents();
-                var actuators = CreateActuatorsFromMap(m_InputAsset.FindActionMap(m_PlayerInput.defaultActionMap), m_BehaviorParameters, null);
-                m_ActionSpec = CombineActuatorActionSpecs(actuators);
+                if (!EditorApplication.isPlaying && m_ActionSpec.NumContinuousActions == 0
+                    && m_ActionSpec.BranchSizes == null
+                    || m_ActionSpec.BranchSizes.Length == 0)
+                {
+                    FindNeededComponents();
+                    var actuators = CreateActuatorsFromMap(m_InputAsset.FindActionMap(m_PlayerInput.defaultActionMap),
+                        m_BehaviorParameters,
+                        null,
+                        InputActuatorEventContext.s_EditorContext);
+                    m_ActionSpec = CombineActuatorActionSpecs(actuators);
+
+                }
 #endif
                 return m_ActionSpec;
             }
             RegisterLayoutBuilder(inputActionMap, m_LayoutName);
             m_Device = InputSystem.AddDevice(m_LayoutName);
 
-            m_Actuators = CreateActuatorsFromMap(inputActionMap, m_BehaviorParameters, m_Device);
+            var context = new InputActuatorEventContext(inputActionMap.actions.Count, m_Device);
+            m_Actuators = CreateActuatorsFromMap(inputActionMap, m_BehaviorParameters, m_Device, context);
 
             UpdateDeviceBinding(m_BehaviorParameters.IsInHeuristicMode());
             inputActionMap.Enable();
 
         internal static IActuator[] CreateActuatorsFromMap(InputActionMap inputActionMap,
             BehaviorParameters behaviorParameters,
-            InputDevice inputDevice)
+            InputDevice inputDevice,
+            InputActuatorEventContext context)
         {
             var actuators = new IActuator[inputActionMap.actions.Count];
             for (var i = 0; i < inputActionMap.actions.Count; i++)
                 var adaptor = (IRLActionInputAdaptor)Activator.CreateInstance(controlTypeToAdaptorType[actionLayout.type]);
-                actuators[i] = new InputActionActuator(inputDevice, behaviorParameters, action, adaptor);
+                actuators[i] = new InputActionActuator(inputDevice, behaviorParameters, action, adaptor, context);
 
                 // Reasonably, the input system starts adding numbers after the first none numbered name
                 // is added.  So for device ID of 0, we use the empty string in the path.
                     action.processors,
                     mlAgentsControlSchemeName);
+                action.bindingMask = InputBinding.MaskByGroup(mlAgentsControlSchemeName);
             }
             return actuators;
         }
             m_PlayerInput = null;
             m_BehaviorParameters = null;
             m_Device = null;
+        }
+
+        int m_ActuatorsWrittenToEvent;
+        NativeArray<byte> m_InputBufferForFrame;
+        InputEventPtr m_InputEventPtrForFrame;
+        public InputEventPtr GetEventForFrame()
+        {
+#if UNITY_EDITOR
+            if (!EditorApplication.isPlaying)
+            {
+                return new InputEventPtr();
+            }
+#endif
+            if (m_ActuatorsWrittenToEvent % m_Actuators.Length == 0 || !m_InputEventPtrForFrame.valid)
+            {
+                m_ActuatorsWrittenToEvent = 0;
+                m_InputEventPtrForFrame = new InputEventPtr();
+                m_InputBufferForFrame = StateEvent.From(m_Device, out m_InputEventPtrForFrame);
+            }
+
+            return m_InputEventPtrForFrame;
+        }
+
+        public void EventProcessedInFrame()
+        {
+#if UNITY_EDITOR
+            if (!EditorApplication.isPlaying)
+            {
+                return;
+            }
+#endif
+            m_ActuatorsWrittenToEvent++;
+            if (m_ActuatorsWrittenToEvent == m_Actuators.Length && m_InputEventPtrForFrame.valid)
+            {
+                InputSystem.QueueEvent(m_InputEventPtrForFrame);
+                m_InputBufferForFrame.Dispose();
+            }
         }
     }
 }

com.unity.ml-agents.extensions/Runtime/Input/Unity.ML-Agents.Extensions.Input.asmdef

     "versionDefines": [
         {
             "name": "com.unity.inputsystem",
-            "expression": "1.1.0-preview",
+            "expression": "1.1.0-preview.3",
-}
+}

com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/ButtonInputActionAdaptorTests.cs

         public void TestQueueEvent()
         {
             var actionBuffers = new ActionBuffers(ActionSegment<float>.Empty, new ActionSegment<int>(new[] { 1 }));
-            m_Adaptor.QueueInputEventForAction(m_Action, m_Control, new ActionSpec(), actionBuffers);
+            var context = new InputActuatorEventContext(1, m_Device);
+            using (context.GetEventForFrame(out var eventPtr))
+            {
+                m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
+            }
             InputSystem.Update();
             var val = m_Action.ReadValue<float>();
             Assert.IsTrue(Mathf.Approximately(1f, val));
         public void TestWriteToHeuristic()
         {
             var actionBuffers = new ActionBuffers(ActionSegment<float>.Empty, new ActionSegment<int>(new[] { 1 }));
-            m_Adaptor.QueueInputEventForAction(m_Action, m_Control, new ActionSpec(), actionBuffers);
+            var context = new InputActuatorEventContext(1, m_Device);
+            using (context.GetEventForFrame(out var eventPtr))
+            {
+                m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
+            }
             InputSystem.Update();
             var buffer = new ActionBuffers(ActionSegment<float>.Empty, new ActionSegment<int>(new[] { 1 }));
             m_Adaptor.WriteToHeuristic(m_Action, buffer);

com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/DoubleInputActionAdaptorTests.cs

         public void TestQueueEvent()
         {
             var actionBuffers = new ActionBuffers(new ActionSegment<float>(new[] { 1f }), ActionSegment<int>.Empty);
-            m_Adaptor.QueueInputEventForAction(m_Action, m_Control, new ActionSpec(), actionBuffers);
+            var context = new InputActuatorEventContext(1, m_Device);
+            using (context.GetEventForFrame(out var eventPtr))
+            {
+                m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
+            }
             InputSystem.Update();
             Assert.IsTrue(Mathf.Approximately(1f, (float)m_Action.ReadValue<double>()));
         }
         {
             var actionBuffers = new ActionBuffers(new ActionSegment<float>(new[] { 1f }), ActionSegment<int>.Empty);
-            m_Adaptor.QueueInputEventForAction(m_Action, m_Control, new ActionSpec(), actionBuffers);
+            var context = new InputActuatorEventContext(1, m_Device);
+            using (context.GetEventForFrame(out var eventPtr))
+            {
+                m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
+            }
             InputSystem.Update();
             var buffer = new ActionBuffers(new ActionSegment<float>(new[] { 1f }), ActionSegment<int>.Empty);
             m_Adaptor.WriteToHeuristic(m_Action, buffer);

com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/FloatInputActionAdapatorTests.cs

         public void TestQueueEvent()
         {
             var actionBuffers = new ActionBuffers(new ActionSegment<float>(new[] { 1f }), ActionSegment<int>.Empty);
-            m_Adaptor.QueueInputEventForAction(m_Action, m_Control, new ActionSpec(), actionBuffers);
+            var context = new InputActuatorEventContext(1, m_Device);
+            using (context.GetEventForFrame(out var eventPtr))
+            {
+                m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
+            }
             InputSystem.Update();
             var val = m_Action.ReadValue<float>();
             Assert.IsTrue(Mathf.Approximately(1f, val));
         public void TestWriteToHeuristic()
         {
             var actionBuffers = new ActionBuffers(new ActionSegment<float>(new[] { 1f }), ActionSegment<int>.Empty);
-            m_Adaptor.QueueInputEventForAction(m_Action, m_Control, new ActionSpec(), actionBuffers);
+            var context = new InputActuatorEventContext(1, m_Device);
+            using (context.GetEventForFrame(out var eventPtr))
+            {
+                m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
+            }
             InputSystem.Update();
             var buffer = new ActionBuffers(new ActionSegment<float>(new[] { 1f }), ActionSegment<int>.Empty);
             m_Adaptor.WriteToHeuristic(m_Action, buffer);

com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/IntegerInputActionAdaptorTests.cs

         public void TestQueueEvent()
         {
             var actionBuffers = new ActionBuffers(ActionSegment<float>.Empty, new ActionSegment<int>(new[] { 1 }));
-            m_Adaptor.QueueInputEventForAction(m_Action, m_Control, new ActionSpec(), actionBuffers);
+            var context = new InputActuatorEventContext(1, m_Device);
+            using (context.GetEventForFrame(out var eventPtr))
+            {
+                m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
+            }
             InputSystem.Update();
             var val = m_Action.ReadValue<int>();
             Assert.IsTrue(val == 1);
         public void TestWriteToHeuristic()
         {
             var actionBuffers = new ActionBuffers(ActionSegment<float>.Empty, new ActionSegment<int>(new[] { 1 }));
-            m_Adaptor.QueueInputEventForAction(m_Action, m_Control, new ActionSpec(), actionBuffers);
+            var context = new InputActuatorEventContext(1, m_Device);
+            using (context.GetEventForFrame(out var eventPtr))
+            {
+                m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
+            }
             InputSystem.Update();
             var buffer = new ActionBuffers(ActionSegment<float>.Empty, new ActionSegment<int>(new int[1]));
             m_Adaptor.WriteToHeuristic(m_Action, buffer);

com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/Vector2InputActionAdaptorTests.cs

         public void TestQueueEvent()
         {
             var actionBuffers = new ActionBuffers(new ActionSegment<float>(new[] { 0f, 1f }), ActionSegment<int>.Empty);
-            m_Adaptor.QueueInputEventForAction(m_Action, m_Control, new ActionSpec(), actionBuffers);
+            var context = new InputActuatorEventContext(1, m_Device);
+            using (context.GetEventForFrame(out var eventPtr))
+            {
+                m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
+            }
             InputSystem.Update();
             var val = m_Action.ReadValue<Vector2>();
             Assert.IsTrue(Mathf.Approximately(0f, val.x));
         public void TestWriteToHeuristic()
         {
             var actionBuffers = new ActionBuffers(new ActionSegment<float>(new[] { 0f, 1f }), ActionSegment<int>.Empty);
-            m_Adaptor.QueueInputEventForAction(m_Action, m_Control, new ActionSpec(), actionBuffers);
+            var context = new InputActuatorEventContext(1, m_Device);
+            using (context.GetEventForFrame(out var eventPtr))
+            {
+                m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
+            }
             InputSystem.Update();
             var buffer = new ActionBuffers(new ActionSegment<float>(new float[2]), ActionSegment<int>.Empty);
             m_Adaptor.WriteToHeuristic(m_Action, buffer);

com.unity.ml-agents.extensions/Tests/Runtime/Input/InputActionActuatorTests.cs

 using Unity.MLAgents.Policies;
 using UnityEngine;
 using UnityEngine.InputSystem;
+using UnityEngine.InputSystem.LowLevel;
-        public bool eventQueued;
+        public bool eventWritten;
         public bool writtenToHeuristic;
 
         public ActionSpec GetActionSpecForInputAction(InputAction action)
 
-        public void QueueInputEventForAction(InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers)
+        public void WriteToInputEventForAction(InputEventPtr eventPtr, InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers)
-            eventQueued = true;
+            eventWritten = true;
         }
 
         public void WriteToHeuristic(InputAction action, in ActionBuffers actionBuffers)
 
         public void Reset()
         {
-            eventQueued = false;
+            eventWritten = false;
             writtenToHeuristic = false;
         }
     }
             m_BehaviorParameters = go.AddComponent<BehaviorParameters>();
             var action = new InputAction("action");
             m_Adaptor = new TestAdaptor();
-            m_Actuator = new InputActionActuator(null, m_BehaviorParameters, action, m_Adaptor);
+            m_Actuator = new InputActionActuator(null, m_BehaviorParameters, action, m_Adaptor, new InputActuatorEventContext(1, InputSystem.AddDevice<Gamepad>()));
         }
 
         [Test]
             m_Actuator.OnActionReceived(new ActionBuffers());
             m_Actuator.Heuristic(new ActionBuffers());
-            Assert.IsFalse(m_Adaptor.eventQueued);
+            Assert.IsFalse(m_Adaptor.eventWritten);
-            Assert.IsFalse(m_Adaptor.eventQueued);
+            Assert.IsFalse(m_Adaptor.eventWritten);
-            Assert.IsTrue(m_Adaptor.eventQueued);
+            Assert.IsTrue(m_Adaptor.eventWritten);
             m_Adaptor.Reset();
 
             Assert.AreEqual(m_Actuator.Name, "InputActionActuator-action");

com.unity.ml-agents.extensions/Tests/Runtime/Input/InputActuatorComponentTests.cs

 
             var device = InputSystem.AddDevice("TestLayout");
 
-            var actuators = InputActuatorComponent.CreateActuatorsFromMap(inputActionMap, m_BehaviorParameters, device);
+            var actuators = InputActuatorComponent.CreateActuatorsFromMap(inputActionMap, m_BehaviorParameters, device, new InputActuatorEventContext());
             Assert.IsTrue(actuators.Length == 2);
             Assert.IsTrue(actuators[0].ActionSpec.Equals(ActionSpec.MakeContinuous(2)));
             Assert.IsTrue(actuators[1].ActionSpec.NumDiscreteActions == 1);

com.unity.ml-agents.extensions/Tests/Runtime/Input/Unity.ML-Agents.Extensions.Input.Tests.Runtime.asmdef

     "versionDefines": [
         {
             "name": "com.unity.inputsystem",
-            "expression": "1.1.0",
+            "expression": "1.1.0-preview.3",
             "define": "MLA_INPUT_TESTS"
         }
     ],

com.unity.ml-agents/CHANGELOG.md

 - The `cattrs` version dependency was updated to allow `>=1.1.0` on Python 3.8 or higher. (#4821)
 
 ### Bug Fixes
-#### com.unity.ml-agents (C#)
+#### com.unity.ml-agents / com.unity.ml-agents.extensions (C#)
+- Fix an issue where queuing InputEvents overwrote data from previous events in the same frame.
 
 ## [1.8.0-preview] - 2021-02-17
 ### Major Changes

config/imitation/Crawler.yaml

 behaviors:
-  CrawlerStatic:
+  Crawler:
     trainer_type: ppo
     hyperparameters:
       batch_size: 2024
         learning_rate: 0.0003
         use_actions: false
         use_vail: false
-        demo_path: Project/Assets/ML-Agents/Examples/Crawler/Demos/ExpertCrawlerSta.demo
+        demo_path: Project/Assets/ML-Agents/Examples/Crawler/Demos/ExpertCrawler.demo
     keep_checkpoints: 5
     max_steps: 10000000
     time_horizon: 1000
-      demo_path: Project/Assets/ML-Agents/Examples/Crawler/Demos/ExpertCrawlerSta.demo
+      demo_path: Project/Assets/ML-Agents/Examples/Crawler/Demos/ExpertCrawler.demo
       steps: 50000
       strength: 0.5
       samples_per_update: 0

config/ppo/FoodCollector.yaml

       learning_rate_schedule: linear
     network_settings:
       normalize: false
-      hidden_units: 128
-      num_layers: 2
+      hidden_units: 256
+      num_layers: 1
       vis_encode_type: simple
     reward_signals:
       extrinsic:

config/ppo/Worm.yaml

 behaviors:
-  WormDynamic:
+  Worm:
     trainer_type: ppo
     hyperparameters:
       batch_size: 2024

config/ppo/Walker.yaml

 behaviors:
-  CrawlerDynamicVariableSpeed:
+  Walker:
     trainer_type: ppo
     hyperparameters:
       batch_size: 2048
         gamma: 0.995
         strength: 1.0
     keep_checkpoints: 5
-    max_steps: 10000000
+    max_steps: 30000000
     time_horizon: 1000
     summary_freq: 30000
     threaded: true

config/ppo/Crawler.yaml

 behaviors:
-  CrawlerDynamic:
+  Crawler:
     trainer_type: ppo
     hyperparameters:
       batch_size: 2048

config/sac/FoodCollector.yaml

       learning_rate: 0.0003
       learning_rate_schedule: constant
       batch_size: 256
-      buffer_size: 500000
+      buffer_size: 2048
       buffer_init_steps: 0
       tau: 0.005
       steps_per_update: 10.0
     network_settings:
       normalize: false
-      hidden_units: 128
-      num_layers: 2
+      hidden_units: 256
+      num_layers: 1
       vis_encode_type: simple
     reward_signals:
       extrinsic:
     max_steps: 2000000
     time_horizon: 64
-    summary_freq: 10000
-    threaded: true
+    summary_freq: 60000
+    threaded: true

config/sac/Worm.yaml

 behaviors:
-  WormDynamic:
+  Worm:
     trainer_type: sac
     hyperparameters:
       learning_rate: 0.0003

config/sac/Walker.yaml

 behaviors:
-  WalkerDynamic:
+  Walker:
     trainer_type: sac
     hyperparameters:
       learning_rate: 0.0003

config/sac/Crawler.yaml

 behaviors:
-  CrawlerDynamic:
+  Crawler:
     trainer_type: sac
     hyperparameters:
       learning_rate: 0.0003

docs/Learning-Environment-Design-Agents.md

 training process learns to control the speed of the Agent through this
 parameter.
 
-The [Reacher example](Learning-Environment-Examples.md#reacher) uses
+The [3DBall example](Learning-Environment-Examples.md#3dball-3d-balance-ball) uses
-![reacher](images/reacher.png)
+![3DBall](images/balance.png)
-These control values are applied as torques to the bodies making up the arm:
+These control values are applied as rotation to the cube:
-        var torqueX = Mathf.Clamp(actionBuffers.ContinuousActions[0], -1f, 1f) * 150f;
-        var torqueZ = Mathf.Clamp(actionBuffers.ContinuousActions[1], -1f, 1f) * 150f;
-        m_RbA.AddTorque(new Vector3(torqueX, 0f, torqueZ));
+        var actionZ = 2f * Mathf.Clamp(actionBuffers.ContinuousActions[0], -1f, 1f);
+        var actionX = 2f * Mathf.Clamp(actionBuffers.ContinuousActions[1], -1f, 1f);
-        torqueX = Mathf.Clamp(actionBuffers.ContinuousActions[2], -1f, 1f) * 150f;
-        torqueZ = Mathf.Clamp(actionBuffers.ContinuousActions[3], -1f, 1f) * 150f;
-        m_RbB.AddTorque(new Vector3(torqueX, 0f, torqueZ));
+        gameObject.transform.Rotate(new Vector3(0, 0, 1), actionZ);
+        gameObject.transform.Rotate(new Vector3(1, 0, 0), actionX);
     }
 ```
 

docs/Learning-Environment-Examples.md

   number of goals.
 - Benchmark Mean Reward: 0.8
 
-## Tennis
-
-![Tennis](images/tennis.png)
-
-- Set-up: Two-player game where agents control rackets to hit a ball over the
-  net.
-- Goal: The agents must hit the ball so that the opponent cannot hit a valid
-  return.
-- Agents: The environment contains two agent with same Behavior Parameters.
-  After training you can set the `Behavior Type` to `Heuristic Only` on one of
-  the Agent's Behavior Parameters to play against your trained model.
-- Agent Reward Function (independent):
-  - +1.0 To the agent that wins the point. An agent wins a point by preventing
-    the opponent from hitting a valid return.
-  - -1.0 To the agent who loses the point.
-- Behavior Parameters:
-  - Vector Observation space: 9 variables corresponding to position, velocity
-    and orientation of ball and racket.
-  - Actions: 3 continuous actions, corresponding to movement
-    toward net or away from net, jumping and rotation.
-  - Visual Observations: None
-- Float Properties: Three
-  - gravity: Magnitude of gravity
-    - Default: 9.81
-    - Recommended Minimum: 6
-    - Recommended Maximum: 20
-  - scale: Specifies the scale of the ball in the 3 dimensions (equal across the
-    three dimensions)
-    - Default: .5
-    - Recommended Minimum: 0.2
-    - Recommended Maximum: 5
-
 ## Push Block
 
 ![Push](images/push.png)
     block).
   - Actions: 1 discrete action branch with 7 actions, corresponding to turn clockwise
     and counterclockwise, move along four different face directions, or do nothing.
-  - Visual Observations (Optional): One first-person camera. Use
-    `VisualPushBlock` scene. **The visual observation version of this
-    environment does not train with the provided default training parameters.**
 - Float Properties: Four
   - block_scale: Scale of the block along the x and z dimensions
     - Default: 2
 - Float Properties: Four
 - Benchmark Mean Reward (Big & Small Wall): 0.8
 
-## Reacher
-
-![Reacher](images/reacher.png)
-
-- Set-up: Double-jointed arm which can move to target locations.
-- Goal: The agents must move its hand to the goal location, and keep it there.
-- Agents: The environment contains 10 agent with same Behavior Parameters.
-- Agent Reward Function (independent):
-  - +0.1 Each step agent's hand is in goal location.
-- Behavior Parameters:
-  - Vector Observation space: 26 variables corresponding to position, rotation,
-    velocity, and angular velocities of the two arm rigid bodies.
-  - Actions: 4 continuous actions, corresponding to torque
-    applicable to two joints.
-  - Visual Observations: None.
-- Float Properties: Five
-  - goal_size: radius of the goal zone
-    - Default: 5
-    - Recommended Minimum: 1
-    - Recommended Maximum: 10
-  - goal_speed: speed of the goal zone around the arm (in radians)
-    - Default: 1
-    - Recommended Minimum: 0.2
-    - Recommended Maximum: 4
-  - gravity
-    - Default: 9.81
-    - Recommended Minimum: 4
-    - Recommended Maximum: 20
-  - deviation: Magnitude of sinusoidal (cosine) deviation of the goal along the
-    vertical dimension
-    - Default: 0
-    - Recommended Minimum: 0
-    - Recommended Maximum: 5
-  - deviation_freq: Frequency of the cosine deviation of the goal along the
-    vertical dimension
-    - Default: 0
-    - Recommended Minimum: 0
-    - Recommended Maximum: 3
-- Benchmark Mean Reward: 30
-
 ## Crawler
 
 ![Crawler](images/crawler.png)
-  - `CrawlerDynamicTarget`- Goal direction is randomized.
-  - `CrawlerDynamicVariableSpeed`- Goal direction and walking speed are randomized.
-  - `CrawlerStaticTarget` - Goal direction is always forward.
-  - `CrawlerStaticVariableSpeed`- Goal direction is always forward. Walking speed is randomized
 - Agents: The environment contains 10 agents with same Behavior Parameters.
 - Agent Reward Function (independent):
   The reward function is now geometric meaning the reward each step is a product
     rotations for joints.
   - Visual Observations: None
 - Float Properties: None
-- Benchmark Mean Reward for `CrawlerDynamicTarget`: 2000
-- Benchmark Mean Reward for `CrawlerDynamicVariableSpeed`: 3000
-- Benchmark Mean Reward for `CrawlerStaticTarget`: 4000
-- Benchmark Mean Reward for `CrawlerStaticVariableSpeed`: 4000
+- Benchmark Mean Reward: 3000
 
 ## Worm
 
 - Goal: The agents must move its body toward the goal direction.
-  - `WormStaticTarget` - Goal direction is always forward.
-  - `WormDynamicTarget`- Goal direction is randomized.
 - Agents: The environment contains 10 agents with same Behavior Parameters.
 - Agent Reward Function (independent):
   The reward function is now geometric meaning the reward each step is a product
     rotations for joints.
   - Visual Observations: None
 - Float Properties: None
-- Benchmark Mean Reward for `WormStaticTarget`: 1200
-- Benchmark Mean Reward for `WormDynamicTarget`: 800
+- Benchmark Mean Reward: 800
 
 ## Food Collector
 
   - -1 for interaction with red spheres
 - Behavior Parameters:
   - Vector Observation space: 53 corresponding to velocity of agent (2), whether
-    agent is frozen and/or shot its laser (2), plus ray-based perception of
-    objects around agent's forward direction (49; 7 raycast angles with 7
-    measurements for each).
+    agent is frozen and/or shot its laser (2), plus grid based perception of
+    objects around agent's forward direction (40 by 40 with 6 different categories).
   - Actions:
     - 3 continuous actions correspond to Forward Motion, Side Motion and Rotation
     - 1 discrete acion branch for Laser with 2 possible actions corresponding to
     objects, goals, and walls.
   - Actions: 1 discrete action Branch, with 4 actions corresponding to agent
     rotation and forward/backward movement.
-  - Visual Observations (Optional): First-person view for the agent. Use
-    `VisualHallway` scene. **The visual observation version of this environment
-    does not train with the provided default training parameters.**
-## Bouncer
-
-![Bouncer](images/bouncer.png)
-
-- Set-up: Environment where the agent needs on-demand decision making. The agent
-  must decide how perform its next bounce only when it touches the ground.
-- Goal: Catch the floating green cube. Only has a limited number of jumps.
-- Agents: The environment contains one agent.
-- Agent Reward Function (independent):
-  - +1 For catching the green cube.
-  - -1 For bouncing out of bounds.
-  - -0.05 Times the action squared. Energy expenditure penalty.
-- Behavior Parameters:
-  - Vector Observation space: 6 corresponding to local position of agent and
-    green cube.
-  - Actions: 3 continuous actions corresponding to agent force applied for
-    the jump.
-  - Visual Observations: None
-- Float Properties: Two
-  - target_scale: The scale of the green cube in the 3 dimensions
-    - Default: 150
-    - Recommended Minimum: 50
-    - Recommended Maximum: 250
-- Benchmark Mean Reward: 10
-
 ## Soccer Twos
 
 ![SoccerTwos](images/soccer.png)
   correspond to articulation of the following body-parts: hips, chest, spine,
   head, thighs, shins, feet, arms, forearms and hands.
 - Goal: The agents must move its body toward the goal direction without falling.
-  - `WalkerDynamic`- Goal direction is randomized.
-  - `WalkerDynamicVariableSpeed`- Goal direction and walking speed are randomized.
-  - `WalkerStatic` - Goal direction is always forward.
-  - `WalkerStaticVariableSpeed` - Goal direction is always forward. Walking
-     speed is randomized
 - Agents: The environment contains 10 independent agents with same Behavior
   Parameters.
 - Agent Reward Function (independent):
     - Default: 8
     - Recommended Minimum: 3
     - Recommended Maximum: 20
-- Benchmark Mean Reward for `WalkerDynamic`: 2500
-- Benchmark Mean Reward for `WalkerDynamicVariableSpeed`: 2500
-- Benchmark Mean Reward for `WalkerStatic`: 3500
-- Benchmark Mean Reward for `WalkerStaticVariableSpeed`: 3500
-
+- Benchmark Mean Reward : 2500
 
 
 ## Pyramids
     state.
   - Actions: 1 discrete action branch, with 4 actions corresponding to agent rotation and
     forward/backward movement.
-  - Visual Observations (Optional): First-person camera per-agent. Us
-    `VisualPyramids` scene. **The visual observation version of this environment
-    does not train with the provided default training parameters.**
 - Float Properties: None
 - Benchmark Mean Reward: 1.75
 

docs/Training-Configuration-File.md

 | `network_settings -> hidden_units`           | (default = `128`) Number of units in the hidden layers of the neural network. Correspond to how many units are in each fully connected layer of the neural network. For simple problems where the correct action is a straightforward combination of the observation inputs, this should be small. For problems where the action is a very complex interaction between the observation variables, this should be larger. <br><br> Typical range: `32` - `512`                                                                                                                                                                                                                                                                                    |
 | `network_settings -> num_layers`             | (default = `2`) The number of hidden layers in the neural network. Corresponds to how many hidden layers are present after the observation input, or after the CNN encoding of the visual observation. For simple problems, fewer layers are likely to train faster and more efficiently. More layers may be necessary for more complex control problems. <br><br> Typical range: `1` - `3`                                                                                                                                                                                                                                                                                                                                                    |
 | `network_settings -> normalize`              | (default = `false`) Whether normalization is applied to the vector observation inputs. This normalization is based on the running average and variance of the vector observation. Normalization can be helpful in cases with complex continuous control problems, but may be harmful with simpler discrete control problems.                                                                                                                                                                                                                                                                                                                                                                                                                       |
-| `network_settings -> vis_encoder_type`       | (default = `simple`) Encoder type for encoding visual observations. <br><br> `simple` (default) uses a simple encoder which consists of two convolutional layers, `nature_cnn` uses the CNN implementation proposed by [Mnih et al.](https://www.nature.com/articles/nature14236), consisting of three convolutional layers, and `resnet` uses the [IMPALA Resnet](https://arxiv.org/abs/1802.01561) consisting of three stacked layers, each with two residual blocks, making a much larger network than the other two. `match3` is a smaller CNN ([Gudmundsoon et al.](https://www.researchgate.net/publication/328307928_Human-Like_Playtesting_with_Deep_Learning)) that is optimized for board games, and can be used down to visual observation sizes of 5x5.                                                                                                                                                                                              |
+| `network_settings -> vis_encode_type`       | (default = `simple`) Encoder type for encoding visual observations. <br><br> `simple` (default) uses a simple encoder which consists of two convolutional layers, `nature_cnn` uses the CNN implementation proposed by [Mnih et al.](https://www.nature.com/articles/nature14236), consisting of three convolutional layers, and `resnet` uses the [IMPALA Resnet](https://arxiv.org/abs/1802.01561) consisting of three stacked layers, each with two residual blocks, making a much larger network than the other two. `match3` is a smaller CNN ([Gudmundsoon et al.](https://www.researchgate.net/publication/328307928_Human-Like_Playtesting_with_Deep_Learning)) that is optimized for board games, and can be used down to visual observation sizes of 5x5.                                                                                                                                                                                              |
 
 
 ## Trainer-specific Configurations

docs/Training-ML-Agents.md

 
     # Configuration of the neural network (common to PPO/SAC)
     network_settings:
-      vis_encoder_type: simple
+      vis_encode_type: simple
       normalize: false
       hidden_units: 128
       num_layers: 2

ml-agents/mlagents/trainers/buffer.py

         else:
             return return_data
 
-    def append(self, element: np.ndarray, padding_value: float = 0.0) -> None:
+    @property
+    def contains_lists(self) -> bool:
+        """
+        Checks whether this AgentBufferField contains List[np.ndarray].
+        """
+        return len(self) > 0 and isinstance(self[0], list)
+
+    def append(self, element: BufferEntry, padding_value: float = 0.0) -> None:
         """
         Adds an element to this list. Also lets you change the padding
         type, so that it can be set on append (e.g. action_masks should
                     " too large given the current number of data points."
                 )
             if batch_size * training_length > len(self):
-                padding = np.array(self[-1], dtype=np.float32) * self.padding_value
+                if self.contains_lists:
+                    padding = []
+                else:
+                    # We want to duplicate the last value in the array, multiplied by the padding_value.
+                    padding = np.array(self[-1], dtype=np.float32) * self.padding_value
                 return [padding] * (training_length - leftover) + self[:]
 
             else:
             dimension is equal to the length of the AgentBufferField.
         """
         if len(self) > 0 and not isinstance(self[0], list):
-            return np.asanyarray(self, dytpe=dtype)
+            return np.asanyarray(self, dtype=dtype)
 
         shape = None
         for _entry in self:

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

             self.policy.update_normalization(agent_buffer_trajectory)
 
         # Get all value estimates
-        value_estimates, value_next, value_memories = self.optimizer.get_trajectory_value_estimates(
+        (
+            value_estimates,
+            value_next,
+            value_memories,
+        ) = self.optimizer.get_trajectory_value_estimates(
             agent_buffer_trajectory,
             trajectory.next_obs,
             trajectory.done_reached and not trajectory.interrupted,
             int(self.hyperparameters.batch_size / self.policy.sequence_length), 1
         )
 
-        advantages = np.array(self.update_buffer[BufferKey.ADVANTAGES].get_batch())
+        advantages = np.array(
+            self.update_buffer[BufferKey.ADVANTAGES].get_batch(), dtype=np.float32
+        )
         self.update_buffer[BufferKey.ADVANTAGES].set(
             (advantages - advantages.mean()) / (advantages.std() + 1e-10)
         )

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

             ]
         ),
     )
-    # Test group entries return Lists of Lists
+
+    # Test padding
+    a = agent_2_buffer[ObsUtil.get_name_at(0)].get_batch(
+        batch_size=None, training_length=4, sequential=True
+    )
+    assert_array(
+        np.array(a),
+        np.array(
+            [
+                [0, 0, 0],
+                [0, 0, 0],
+                [0, 0, 0],
+                [201, 202, 203],
+                [211, 212, 213],
+                [221, 222, 223],
+                [231, 232, 233],
+                [241, 242, 243],
+                [251, 252, 253],
+                [261, 262, 263],
+                [271, 272, 273],
+                [281, 282, 283],
+            ]
+        ),
+    )
+    # Test group entries return Lists of Lists. Make sure to pad properly!
-        batch_size=2, training_length=1, sequential=True
+        batch_size=None, training_length=4, sequential=True
-    for _group_entry in a:
+    for _group_entry in a[:3]:
+        assert len(_group_entry) == 0
+    for _group_entry in a[3:]:
         assert len(_group_entry) == 3
 
     agent_1_buffer.reset_agent()
     padded = c.padded_to_batch(pad_value=3)
     assert np.array_equal(padded[0], np.array([1, 1, 1, 1]))
     assert np.array_equal(padded[1], np.array([2, 2, 3, 3]))
+
+    # Make sure it doesn't fail when the field isn't a list
+    padded_a = a.padded_to_batch()
+    assert np.array_equal(padded_a, a)
 
 
 def fakerandint(values):

Project/Assets/ML-Agents/Examples/Walker/Prefabs/Platforms/Platform.prefab

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com.unity.ml-agents.extensions/Runtime/Input/InputActuatorEventContext.cs

+#if MLA_INPUT_SYSTEM && UNITY_2019_4_OR_NEWER
+using System;
+using Unity.Collections;
+using UnityEngine.InputSystem;
+using UnityEngine.InputSystem.LowLevel;
+#if UNITY_EDITOR
+using UnityEditor;
+#endif
+
+namespace Unity.MLAgents.Extensions.Input
+{
+    /// <summary>
+    /// This interface is passed to InputActionActuators to allow them to write to InputEvents.
+    /// The way this interface should be used is to request the <see cref="InputEventPtr"/> by calling
+    /// <see cref="GetEventForFrame"/> then call <see cref="EventProcessedInFrame"/> before returning from
+    /// </summary>
+    public class InputActuatorEventContext : IDisposable
+    {
+
+        /// <summary>
+        /// The number of times to allow the use of an event before queuing it in the InputSystem.
+        /// </summary>
+        public readonly int NumTimesToProcess;
+        public readonly InputDevice InputDevice;
+        NativeArray<byte> m_EventBuffer;
+        InputEventPtr m_Ptr;
+        int m_Count;
+
+#if UNITY_EDITOR
+        public static InputActuatorEventContext s_EditorContext = new InputActuatorEventContext();
+#endif
+
+        public InputActuatorEventContext(int numTimesToProcess = 1, InputDevice device = null)
+        {
+            NumTimesToProcess = numTimesToProcess;
+            InputDevice = device;
+            m_Count = 0;
+            m_Ptr = new InputEventPtr();
+            m_EventBuffer = new NativeArray<byte>();
+        }
+
+        /// <summary>
+        /// Returns the <see cref="InputEventPtr"/> to write to for the current frame.
+        /// </summary>
+        /// <returns>The <see cref="InputEventPtr"/> to write to for the current frame.</returns>
+        public IDisposable GetEventForFrame(out InputEventPtr eventPtr)
+        {
+#if UNITY_EDITOR
+            if (!EditorApplication.isPlaying)
+            {
+                eventPtr = new InputEventPtr();
+            }
+#endif
+            if (m_Count % NumTimesToProcess == 0)
+            {
+                m_Count = 0;
+                m_EventBuffer = StateEvent.From(InputDevice, out m_Ptr);
+            }
+            eventPtr = m_Ptr;
+            return this;
+        }
+
+        public void Dispose()
+        {
+#if UNITY_EDITOR
+            if (!EditorApplication.isPlaying)
+            {
+                return;
+            }
+#endif
+            m_Count++;
+            if (m_Count == NumTimesToProcess && m_Ptr.valid)
+            {
+                InputSystem.QueueEvent(m_Ptr);
+                m_EventBuffer.Dispose();
+            }
+
+        }
+    }
+}
+#endif // MLA_INPUT_SYSTEM && UNITY_2019_4_OR_NEWER

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