Merge branch 'develop-policyonly' into develop-sac-targetq

4 年前 · f9ff3efe
--- a/Project/Assets/ML-Agents/Examples/FoodCollector/Scenes/GridFoodCollector.unity
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--- a/README.md
+++ b/README.md

 # Unity ML-Agents Toolkit

-[![docs badge](https://img.shields.io/badge/docs-reference-blue.svg)](https://github.com/Unity-Technologies/ml-agents/tree/release_6_docs/docs/)
+[![docs badge](https://img.shields.io/badge/docs-reference-blue.svg)](https://github.com/Unity-Technologies/ml-agents/tree/release_7_docs/docs/)

 [![license badge](https://img.shields.io/badge/license-Apache--2.0-green.svg)](LICENSE)


 ## Releases & Documentation

-**Our latest, stable release is `Release 6`. Click
-[here](https://github.com/Unity-Technologies/ml-agents/tree/release_6_docs/docs/Readme.md)
+**Our latest, stable release is `Release 7`. Click
+[here](https://github.com/Unity-Technologies/ml-agents/tree/release_7_docs/docs/Readme.md)
 to get started with the latest release of ML-Agents.**

 The table below lists all our releases, including our `master` branch which is
 | **Version** | **Release Date** | **Source** | **Documentation** | **Download** |
 |:-------:|:------:|:-------------:|:-------:|:------------:|
 | **master (unstable)** | -- | [source](https://github.com/Unity-Technologies/ml-agents/tree/master) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/master/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/master.zip) |
-| **Release 6** | **August 12, 2020** | **[source](https://github.com/Unity-Technologies/ml-agents/tree/release_6)** | **[docs](https://github.com/Unity-Technologies/ml-agents/tree/release_6_docs/docs/Readme.md)** | **[download](https://github.com/Unity-Technologies/ml-agents/archive/release_6.zip)** |
-| **Release 5** | **July 31, 2020** | **[source](https://github.com/Unity-Technologies/ml-agents/tree/release_5)** | **[docs](https://github.com/Unity-Technologies/ml-agents/tree/release_5_docs/docs/Readme.md)** | **[download](https://github.com/Unity-Technologies/ml-agents/archive/release_5.zip)** |
+| **Release 7** | **September 16, 2020** | **[source](https://github.com/Unity-Technologies/ml-agents/tree/release_7)** | **[docs](https://github.com/Unity-Technologies/ml-agents/tree/release_7_docs/docs/Readme.md)** | **[download](https://github.com/Unity-Technologies/ml-agents/archive/release_7.zip)** |
+| **Release 6** | August 12, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/release_6) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/release_6_docs/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/release_6.zip) |
+| **Release 5** | July 31, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/release_5) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/release_5_docs/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/release_5.zip) |
-| **0.15.1** | March 30, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/0.15.1) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/0.15.1/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/0.15.1.zip) |
-| **0.15.0** | March 18, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/0.15.0) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/0.15.0/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/0.15.0.zip) |

 ## Citation

--- a/com.unity.ml-agents.extensions/Runtime/Sensors/GridSensor.cs
+++ b/com.unity.ml-agents.extensions/Runtime/Sensors/GridSensor.cs
        /// <returns>byte[] containing the compressed observation of the grid observation</returns>
        public byte[] GetCompressedObservation()
        {
-            // Timer stack is in accessable due to its protection level
-            //using (TimerStack.Instance.Scoped("GridSensor.GetCompressedObservation"))
+            using (TimerStack.Instance.Scoped("GridSensor.GetCompressedObservation"))
            {
                Perceive(); // Fill the perception buffer with observed data

        public float[] Perceive()
        {
            Reset();
-            // TODO: make these part of the class
-            Collider[] foundColliders = null;
-            Vector3 cellCenter = Vector3.zero;
+            using (TimerStack.Instance.Scoped("GridSensor.Perceive"))
+            {
+                // TODO: make these part of the class
+                Collider[] foundColliders = null;
+                Vector3 cellCenter = Vector3.zero;
-            Vector3 halfCellScale = new Vector3(CellScaleX / 2f, CellScaleY, CellScaleZ / 2f);
+                Vector3 halfCellScale = new Vector3(CellScaleX / 2f, CellScaleY, CellScaleZ / 2f);
-            for (int cellIndex = 0; cellIndex < NumCells; cellIndex++)
-            {
-                if (RotateToAgent)
+                for (int cellIndex = 0; cellIndex < NumCells; cellIndex++)
-                    cellCenter = transform.TransformPoint(CellPoints[cellIndex]);
-                    foundColliders = Physics.OverlapBox(cellCenter, halfCellScale, transform.rotation, ObserveMask);
-                }
-                else
-                {
-                    cellCenter = transform.position + CellPoints[cellIndex];
-                    foundColliders = Physics.OverlapBox(cellCenter, halfCellScale, Quaternion.identity, ObserveMask);
-                }
+                    if (RotateToAgent)
+                    {
+                        cellCenter = transform.TransformPoint(CellPoints[cellIndex]);
+                        foundColliders = Physics.OverlapBox(cellCenter, halfCellScale, transform.rotation, ObserveMask);
+                    }
+                    else
+                    {
+                        cellCenter = transform.position + CellPoints[cellIndex];
+                        foundColliders = Physics.OverlapBox(cellCenter, halfCellScale, Quaternion.identity, ObserveMask);
+                    }
-                if (foundColliders != null && foundColliders.Length > 0)
-                {
-                    ParseColliders(foundColliders, cellIndex, cellCenter);
+                    if (foundColliders != null && foundColliders.Length > 0)
+                    {
+                        ParseColliders(foundColliders, cellIndex, cellCenter);
+                    }
                }
            }

        /// <inheritdoc/>
        public int Write(ObservationWriter writer)
        {
-            // Timer stack is in accessable due to its protection level
-            //    using (TimerStack.Instance.Scoped("GridSensor.WriteToTensor"))
+            using (TimerStack.Instance.Scoped("GridSensor.WriteToTensor"))
            {
                Perceive();

--- a/com.unity.ml-agents/CHANGELOG.md
+++ b/com.unity.ml-agents/CHANGELOG.md
 ### Bug Fixes
 #### com.unity.ml-agents (C#)
 #### ml-agents / ml-agents-envs / gym-unity (Python)
- - Fixed a bug in exporting Pytorch models when using multiple discrete actions. (#4491)


 ## [1.4.0-preview] - 2020-09-16

 ### Minor Changes
 #### com.unity.ml-agents (C#)
- Update Barracuda to 1.1.0-preview (#4208)
+- The `IActuator` interface and `ActuatorComponent` abstract class were added.
+These are analogous to `ISensor` and `SensorComponent`, but for applying actions
+for an Agent. They allow you to control the action space more programmatically
+than defining the actions in the Agent's Behavior Parameters. See
+[BasicActuatorComponent.cs](https://github.com/Unity-Technologies/ml-agents/blob/release_7_docs/Project/Assets/ML-Agents/Examples/Basic/Scripts/BasicActuatorComponent.cs)
+ for an example of how to use them. (#4297, #4315)
+- Update Barracuda to 1.1.1-preview (#4482)
- GridSensor was added to the com.unity.ml-agents.extensions package. Thank you
+- GridSensor was added to the `com.unity.ml-agents.extensions` package. Thank you
 to Jaden Travnik from Eidos Montreal for the contribution! (#4399)
 - Added `Agent.EpisodeInterrupted()`, which can be used to reset the agent when
 it has reached a user-determined maximum number of steps. This behaves similarly
 `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)
+[the PyTorch website](https://pytorch.org/) for installation instructions and
+[the relevant ML-Agents docs](https://github.com/Unity-Technologies/ml-agents/blob/release_7_docs/docs/Training-ML-Agents.md#using-pytorch-experimental) for usage. (#4335)
-ISensor.GetCompressedObservation(), this can be done by writing 3 channels at a
+`ISensor.GetCompressedObservation()`, this can be done by writing 3 channels at a
-a compatible version of the trainer.
+a compatible version of the trainer. (#4462)
+- Improved the executable detection logic for environments on Windows. (#4485)
- The package dependencies were updated to include the built-in packages that are used also. (#4384)
+- Previously, `com.unity.ml-agents` was not declaring built-in packages as
+dependencies in its package.json. The relevant dependencies are now listed. (#4384)
+- Agents no longer try to send observations when they become disabled if the
+Academy has been shut down. (#4489)
+- A bug in the observation normalizer that would cause rewards to decrease
+when using `--resume` was fixed. (#4463)
+- Fixed a bug in exporting Pytorch models when using multiple discrete actions. (#4491)

 ## [1.3.0-preview] - 2020-08-12

--- a/com.unity.ml-agents/Documentation~/com.unity.ml-agents.md
+++ b/com.unity.ml-agents/Documentation~/com.unity.ml-agents.md
 [unity ML-Agents Toolkit]: https://github.com/Unity-Technologies/ml-agents
 [unity inference engine]: https://docs.unity3d.com/Packages/com.unity.barracuda@latest/index.html
 [package manager documentation]: https://docs.unity3d.com/Manual/upm-ui-install.html
-[installation instructions]: https://github.com/Unity-Technologies/ml-agents/blob/release_6_docs/docs/Installation.md
+[installation instructions]: https://github.com/Unity-Technologies/ml-agents/blob/release_7_docs/docs/Installation.md
 [github repository]: https://github.com/Unity-Technologies/ml-agents
 [python package]: https://github.com/Unity-Technologies/ml-agents
 [execution order of event functions]: https://docs.unity3d.com/Manual/ExecutionOrder.html
--- a/com.unity.ml-agents/Documentation~/filter.yml
+++ b/com.unity.ml-agents/Documentation~/filter.yml
    uidRegex: ^Unity.MLAgents\.Tests\.Communicator$
    type: Namespace
 - exclude:
+    uidRegex: ^Unity.MLAgents\.Tests\.Actuators$
+    type: Namespace
+- exclude:
    uidRegex: ^Unity.MLAgents\.Editor$
    type: Namespace
 - exclude:
--- a/com.unity.ml-agents/Runtime/Academy.cs
+++ b/com.unity.ml-agents/Runtime/Academy.cs
 * API. For more information on each of these entities, in addition to how to
 * set-up a learning environment and train the behavior of characters in a
 * Unity scene, please browse our documentation pages on GitHub:
- * https://github.com/Unity-Technologies/ml-agents/tree/release_6_docs/docs/
+ * https://github.com/Unity-Technologies/ml-agents/tree/release_7_docs/docs/
 */

 namespace Unity.MLAgents
    /// fall back to inference or heuristic decisions. (You can also set agents to always use
    /// inference or heuristics.)
    /// </remarks>
-    [HelpURL("https://github.com/Unity-Technologies/ml-agents/tree/release_6_docs/" +
+    [HelpURL("https://github.com/Unity-Technologies/ml-agents/tree/release_7_docs/" +
        "docs/Learning-Environment-Design.md")]
    public class Academy : IDisposable
    {
--- a/com.unity.ml-agents/Runtime/Actuators/ActionSegment.cs
+++ b/com.unity.ml-agents/Runtime/Actuators/ActionSegment.cs
            System.Array.Clear(Array, Offset, Length);
        }

-        /// <inheritdoc cref="IEnumerable{T}.GetEnumerator"/>
+        /// <inheritdoc/>
-        /// <inheritdoc cref="IEnumerable{T}"/>
+        /// <inheritdoc/>
-        /// <inheritdoc cref="ValueType.Equals(object)"/>
+        /// <inheritdoc/>
        public override bool Equals(object obj)
        {
            if (!(obj is ActionSegment<T>))
            return Equals((ActionSegment<T>)obj);
        }

-        /// <inheritdoc cref="IEquatable{T}.Equals(T)"/>
+        /// <inheritdoc/>
-        /// <inheritdoc cref="ValueType.GetHashCode"/>
+        /// <inheritdoc/>
        public override int GetHashCode()
        {
            unchecked
--- a/com.unity.ml-agents/Runtime/Actuators/ActuatorDiscreteActionMask.cs
+++ b/com.unity.ml-agents/Runtime/Actuators/ActuatorDiscreteActionMask.cs
            m_BranchSizes = branchSizes;
        }

-        /// <inheritdoc cref="IDiscreteActionMask.WriteMask"/>
+        /// <inheritdoc/>
        public void WriteMask(int branch, IEnumerable<int> actionIndices)
        {
            LazyInitialize();
            }
        }

-        /// <inheritdoc cref="IDiscreteActionMask.GetMask"/>
+        /// <inheritdoc/>
        public bool[] GetMask()
        {
 #if DEBUG
--- a/com.unity.ml-agents/Runtime/Actuators/ActuatorManager.cs
+++ b/com.unity.ml-agents/Runtime/Actuators/ActuatorManager.cs
            m_DiscreteActionMask.ResetMask();
        }

-
        /// <summary>
        /// Sorts the <see cref="IActuator"/>s according to their <see cref="IActuator.Name"/> value.
        /// </summary>
         * IList implementation that delegates to m_Actuators List.                      *
         *********************************************************************************/

-        /// <summary>
-        /// <inheritdoc cref="IEnumerable{T}.GetEnumerator"/>
-        /// </summary>
+        /// <inheritdoc/>
-        /// <summary>
-        /// <inheritdoc cref="IList{T}.GetEnumerator"/>
-        /// </summary>
+        /// <inheritdoc/>
-        /// <summary>
-        /// <inheritdoc cref="ICollection{T}.Add"/>
-        /// </summary>
-        /// <param name="item"></param>
+        /// <inheritdoc/>
        public void Add(IActuator item)
        {
            Debug.Assert(m_ReadyForExecution == false,
        }

-        /// <summary>
-        /// <inheritdoc cref="ICollection{T}.Clear"/>
-        /// </summary>
+        /// <inheritdoc/>
        public void Clear()
        {
            Debug.Assert(m_ReadyForExecution == false,
        }

-        /// <summary>
-        /// <inheritdoc cref="ICollection{T}.Contains"/>
-        /// </summary>
+        /// <inheritdoc/>
-        /// <summary>
-        /// <inheritdoc cref="ICollection{T}.CopyTo"/>
-        /// </summary>
+        /// <inheritdoc/>
-        /// <summary>
-        /// <inheritdoc cref="ICollection{T}.Remove"/>
-        /// </summary>
+        /// <inheritdoc/>
        public bool Remove(IActuator item)
        {
            Debug.Assert(m_ReadyForExecution == false,
            return false;
        }

-        /// <summary>
-        /// <inheritdoc cref="ICollection{T}.Count"/>
-        /// </summary>
+        /// <inheritdoc/>
-        /// <summary>
-        /// <inheritdoc cref="ICollection{T}.IsReadOnly"/>
-        /// </summary>
+        /// <inheritdoc/>
-        /// <summary>
-        /// <inheritdoc cref="IList{T}.IndexOf"/>
-        /// </summary>
+        /// <inheritdoc/>
-        /// <summary>
-        /// <inheritdoc cref="IList{T}.Insert"/>
-        /// </summary>
+        /// <inheritdoc/>
        public void Insert(int index, IActuator item)
        {
            Debug.Assert(m_ReadyForExecution == false,
        }

-        /// <summary>
-        /// <inheritdoc cref="IList{T}.RemoveAt"/>
-        /// </summary>
+        /// <inheritdoc/>
        public void RemoveAt(int index)
        {
            Debug.Assert(m_ReadyForExecution == false,
            m_Actuators.RemoveAt(index);
        }

-        /// <summary>
-        /// <inheritdoc cref="IList{T}.this"/>
-        /// </summary>
+        /// <inheritdoc/>
        public IActuator this[int index]
        {
            get => m_Actuators[index];
--- a/com.unity.ml-agents/Runtime/Actuators/IActionReceiver.cs
+++ b/com.unity.ml-agents/Runtime/Actuators/IActionReceiver.cs
            DiscreteActions.Clear();
        }

-        /// <inheritdoc cref="ValueType.Equals(object)"/>
+        /// <inheritdoc/>
        public override bool Equals(object obj)
        {
            if (!(obj is ActionBuffers))
                ab.DiscreteActions.SequenceEqual(DiscreteActions);
        }

-        /// <inheritdoc cref="ValueType.GetHashCode"/>
+        /// <inheritdoc/>
        public override int GetHashCode()
        {
            unchecked
        ///
        /// See [Agents - Actions] for more information on masking actions.
        ///
-        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_4_docs/docs/Learning-Environment-Design-Agents.md#actions
+        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_7_docs/docs/Learning-Environment-Design-Agents.md#actions
        /// </remarks>
        /// <seealso cref="IActionReceiver.OnActionReceived"/>
        void WriteDiscreteActionMask(IDiscreteActionMask actionMask);
--- a/com.unity.ml-agents/Runtime/Actuators/IDiscreteActionMask.cs
+++ b/com.unity.ml-agents/Runtime/Actuators/IDiscreteActionMask.cs
        ///
        /// See [Agents - Actions] for more information on masking actions.
        ///
-        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_2_docs/docs/Learning-Environment-Design-Agents.md#actions
+        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_7_docs/docs/Learning-Environment-Design-Agents.md#actions
        /// </remarks>
        /// <param name="branch">The branch for which the actions will be masked.</param>
        /// <param name="actionIndices">The indices of the masked actions.</param>
--- a/com.unity.ml-agents/Runtime/Actuators/VectorActuator.cs
+++ b/com.unity.ml-agents/Runtime/Actuators/VectorActuator.cs
            m_ActionReceiver.WriteDiscreteActionMask(actionMask);
        }

-        /// <summary>
-        /// <inheritdoc cref="IActionReceiver.ActionSpec"/>
-        /// </summary>
+        /// <inheritdoc/>
        public ActionSpec ActionSpec { get; }

        /// <inheritdoc />
--- a/com.unity.ml-agents/Runtime/Agent.cs
+++ b/com.unity.ml-agents/Runtime/Agent.cs
    /// [OnDisable()]: https://docs.unity3d.com/ScriptReference/MonoBehaviour.OnDisable.html]
    /// [OnBeforeSerialize()]: https://docs.unity3d.com/ScriptReference/MonoBehaviour.OnBeforeSerialize.html
    /// [OnAfterSerialize()]: https://docs.unity3d.com/ScriptReference/MonoBehaviour.OnAfterSerialize.html
-    /// [Agents]: https://github.com/Unity-Technologies/ml-agents/blob/release_6_docs/docs/Learning-Environment-Design-Agents.md
-    /// [Reinforcement Learning in Unity]: https://github.com/Unity-Technologies/ml-agents/blob/release_6_docs/docs/Learning-Environment-Design.md
+    /// [Agents]: https://github.com/Unity-Technologies/ml-agents/blob/release_7_docs/docs/Learning-Environment-Design-Agents.md
+    /// [Reinforcement Learning in Unity]: https://github.com/Unity-Technologies/ml-agents/blob/release_7_docs/docs/Learning-Environment-Design.md
-    /// [Unity ML-Agents Toolkit manual]: https://github.com/Unity-Technologies/ml-agents/blob/release_6_docs/docs/Readme.md
+    /// [Unity ML-Agents Toolkit manual]: https://github.com/Unity-Technologies/ml-agents/blob/release_7_docs/docs/Readme.md
-    [HelpURL("https://github.com/Unity-Technologies/ml-agents/blob/release_6_docs/" +
+    [HelpURL("https://github.com/Unity-Technologies/ml-agents/blob/release_7_docs/" +
        "docs/Learning-Environment-Design-Agents.md")]
    [Serializable]
    [RequireComponent(typeof(BehaviorParameters))]
                Academy.Instance.DecideAction -= DecideAction;
                Academy.Instance.AgentAct -= AgentStep;
                Academy.Instance.AgentForceReset -= _AgentReset;
+                NotifyAgentDone(DoneReason.Disabled);
-            NotifyAgentDone(DoneReason.Disabled);
            m_Brain?.Dispose();
            m_Initialized = false;
        }
        /// for information about mixing reward signals from curiosity and Generative Adversarial
        /// Imitation Learning (GAIL) with rewards supplied through this method.
        ///
-        /// [Agents - Rewards]: https://github.com/Unity-Technologies/ml-agents/blob/release_6_docs/docs/Learning-Environment-Design-Agents.md#rewards
-        /// [Reward Signals]: https://github.com/Unity-Technologies/ml-agents/blob/release_6_docs/docs/ML-Agents-Overview.md#a-quick-note-on-reward-signals
+        /// [Agents - Rewards]: https://github.com/Unity-Technologies/ml-agents/blob/release_7_docs/docs/Learning-Environment-Design-Agents.md#rewards
+        /// [Reward Signals]: https://github.com/Unity-Technologies/ml-agents/blob/release_7_docs/docs/ML-Agents-Overview.md#a-quick-note-on-reward-signals
        /// </remarks>
        /// <param name="reward">The new value of the reward.</param>
        public void SetReward(float reward)
        /// for information about mixing reward signals from curiosity and Generative Adversarial
        /// Imitation Learning (GAIL) with rewards supplied through this method.
        ///
-        /// [Agents - Rewards]: https://github.com/Unity-Technologies/ml-agents/blob/release_6_docs/docs/Learning-Environment-Design-Agents.md#rewards
-        /// [Reward Signals]: https://github.com/Unity-Technologies/ml-agents/blob/release_6_docs/docs/ML-Agents-Overview.md#a-quick-note-on-reward-signals
+        /// [Agents - Rewards]: https://github.com/Unity-Technologies/ml-agents/blob/release_7_docs/docs/Learning-Environment-Design-Agents.md#rewards
+        /// [Reward Signals]: https://github.com/Unity-Technologies/ml-agents/blob/release_7_docs/docs/ML-Agents-Overview.md#a-quick-note-on-reward-signals
        ///</remarks>
        /// <param name="increment">Incremental reward value.</param>
        public void AddReward(float increment)
        /// implementing a simple heuristic function can aid in debugging agent actions and interactions
        /// with its environment.
        ///
-        /// [Demonstration Recorder]: https://github.com/Unity-Technologies/ml-agents/blob/release_6_docs/docs/Learning-Environment-Design-Agents.md#recording-demonstrations
-        /// [Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_6_docs/docs/Learning-Environment-Design-Agents.md#actions
+        /// [Demonstration Recorder]: https://github.com/Unity-Technologies/ml-agents/blob/release_7_docs/docs/Learning-Environment-Design-Agents.md#recording-demonstrations
+        /// [Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_7_docs/docs/Learning-Environment-Design-Agents.md#actions
        /// [GameObject]: https://docs.unity3d.com/Manual/GameObjects.html
        /// </remarks>
        /// <example>
        /// For more information about observations, see [Observations and Sensors].
        ///
        /// [GameObject]: https://docs.unity3d.com/Manual/GameObjects.html
-        /// [Observations and Sensors]: https://github.com/Unity-Technologies/ml-agents/blob/release_6_docs/docs/Learning-Environment-Design-Agents.md#observations-and-sensors
+        /// [Observations and Sensors]: https://github.com/Unity-Technologies/ml-agents/blob/release_7_docs/docs/Learning-Environment-Design-Agents.md#observations-and-sensors
        /// </remarks>
        public virtual void CollectObservations(VectorSensor sensor)
        {
        /// Returns a read-only view of the observations that were generated in
        /// <see cref="CollectObservations(VectorSensor)"/>. This is mainly useful inside of a
-        /// <see cref="Heuristic(float[], int[])"/> method to avoid recomputing the observations.
+        /// <see cref="Heuristic(in ActionBuffers)"/> method to avoid recomputing the observations.
        /// </summary>
        /// <returns>A read-only view of the observations list.</returns>
        public ReadOnlyCollection<float> GetObservations()
        ///
        /// See [Agents - Actions] for more information on masking actions.
        ///
-        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_6_docs/docs/Learning-Environment-Design-Agents.md#actions
+        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_7_docs/docs/Learning-Environment-Design-Agents.md#actions
        /// </remarks>
        /// <seealso cref="IActionReceiver.OnActionReceived"/>
        public virtual void WriteDiscreteActionMask(IDiscreteActionMask actionMask)
        /// three values in the action array to use as the force components. During
        /// training, the agent's  policy learns to set those particular elements of
        /// the array to maximize the training rewards the agent receives. (Of course,
-        /// if you implement a <seealso cref="Heuristic(float[], int[])"/> function, it must use the same
+        /// if you implement a <seealso cref="Heuristic(in ActionBuffers)"/> function, it must use the same
        /// elements of the action array for the same purpose since there is no learning
        /// involved.)
        ///
        ///
        /// For more information about implementing agent actions see [Agents - Actions].
        ///
-        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_6_docs/docs/Learning-Environment-Design-Agents.md#actions
+        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_7_docs/docs/Learning-Environment-Design-Agents.md#actions
        /// </remarks>
        /// <param name="actions">
        /// Struct containing the buffers of actions to be executed at this step.
--- a/com.unity.ml-agents/Runtime/Demonstrations/DemonstrationRecorder.cs
+++ b/com.unity.ml-agents/Runtime/Demonstrations/DemonstrationRecorder.cs
    /// See [Imitation Learning - Recording Demonstrations] for more information.
    ///
    /// [GameObject]: https://docs.unity3d.com/Manual/GameObjects.html
-    /// [Imitation Learning - Recording Demonstrations]: https://github.com/Unity-Technologies/ml-agents/blob/release_6_docs/docs//Learning-Environment-Design-Agents.md#recording-demonstrations
+    /// [Imitation Learning - Recording Demonstrations]: https://github.com/Unity-Technologies/ml-agents/blob/release_7_docs/docs//Learning-Environment-Design-Agents.md#recording-demonstrations
    /// </remarks>
    [RequireComponent(typeof(Agent))]
    [AddComponentMenu("ML Agents/Demonstration Recorder", (int)MenuGroup.Default)]
--- a/com.unity.ml-agents/Runtime/DiscreteActionMasker.cs
+++ b/com.unity.ml-agents/Runtime/DiscreteActionMasker.cs
        ///
        /// See [Agents - Actions] for more information on masking actions.
        ///
-        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_6_docs/docs/Learning-Environment-Design-Agents.md#actions
+        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_7_docs/docs/Learning-Environment-Design-Agents.md#actions
        /// </remarks>
        /// <param name="branch">The branch for which the actions will be masked.</param>
        /// <param name="actionIndices">The indices of the masked actions.</param>
--- a/com.unity.ml-agents/package.json
+++ b/com.unity.ml-agents/package.json
  "unity": "2018.4",
  "description": "Use state-of-the-art machine learning to create intelligent character behaviors in any Unity environment (games, robotics, film, etc.).",
  "dependencies": {
-    "com.unity.barracuda": "1.1.0-preview",
+    "com.unity.barracuda": "1.1.1-preview",
    "com.unity.modules.imageconversion": "1.0.0",
    "com.unity.modules.jsonserialize": "1.0.0",
    "com.unity.modules.physics": "1.0.0",
--- a/docs/Installation-Anaconda-Windows.md
+++ b/docs/Installation-Anaconda-Windows.md
 the ml-agents Conda environment by typing `activate ml-agents`)_:

 ```sh
-git clone --branch release_6 https://github.com/Unity-Technologies/ml-agents.git
+git clone --branch release_7 https://github.com/Unity-Technologies/ml-agents.git
-The `--branch release_6` option will switch to the tag of the latest stable
+The `--branch release_7` option will switch to the tag of the latest stable
 release. Omitting that will get the `master` branch which is potentially
 unstable.

--- a/docs/Installation.md
+++ b/docs/Installation.md
 of our tutorials / guides assume you have access to our example environments).

 ```sh
-git clone --branch release_6 https://github.com/Unity-Technologies/ml-agents.git
+git clone --branch release_7 https://github.com/Unity-Technologies/ml-agents.git
-The `--branch release_6` option will switch to the tag of the latest stable
+The `--branch release_7` option will switch to the tag of the latest stable
 release. Omitting that will get the `master` branch which is potentially
 unstable.

 ML-Agents Toolkit for your purposes. If you plan to contribute those changes
-back, make sure to clone the `master` branch (by omitting `--branch release_6`
+back, make sure to clone the `master` branch (by omitting `--branch release_7`
 from the command above). See our
 [Contributions Guidelines](../com.unity.ml-agents/CONTRIBUTING.md) for more
 information on contributing to the ML-Agents Toolkit.
--- a/docs/Learning-Environment-Executable.md
+++ b/docs/Learning-Environment-Executable.md
 - You can exchange executable with other people without having to share your
  entire repository.
 - You can put your executable on a remote machine for faster training.
- You can use `Headless` mode for faster training.
+- You can use `Server Build` (`Headless`) mode for faster training (as long as the executable does not need rendering).
 - You can keep using the Unity Editor for other tasks while the agents are
  training.

 1. Open Player Settings (menu: **Edit** > **Project Settings** > **Player**).
 1. Under **Resolution and Presentation**:
   - Ensure that **Run in Background** is Checked.
-   - Ensure that **Display Resolution Dialog** is set to Disabled.
+   - Ensure that **Display Resolution Dialog** is set to Disabled. (Note: this
+    setting may not be available in newer versions of the editor.)
 1. Open the Build Settings window (menu:**File** > **Build Settings**).
 1. Choose your target platform.
   - (optional) Select “Development Build” to
   - `<run-identifier>` is a string used to separate the results of different
     training runs

-For example, if you are training with a 3DBall executable you exported to the
+For example, if you are training with a 3DBall executable, and you saved it to
 the directory where you installed the ML-Agents Toolkit, run:

 ```sh
--- a/docs/Training-ML-Agents.md
+++ b/docs/Training-ML-Agents.md
    - [Curriculum Learning](#curriculum)
      - [Training with a Curriculum](#training-with-a-curriculum)
  - [Training Using Concurrent Unity Instances](#training-using-concurrent-unity-instances)
+  - [Using PyTorch (Experimental)](#using-pytorch-experimental)

 For a broad overview of reinforcement learning, imitation learning and all the
 training scenarios, methods and options within the ML-Agents Toolkit, see
      save_steps: 50000
      swap_steps: 2000
      team_change: 100000
+
+    # use TensorFlow backend
+    framework: tensorflow
 ```

 Here is an equivalent file if we use an SAC trainer instead. Notice that the
 - **Result Variation Using Concurrent Unity Instances** - If you keep all the
  hyperparameters the same, but change `--num-envs=<n>`, the results and model
  would likely change.
+
+### Using PyTorch (Experimental)
+
+ML-Agents, by default, uses TensorFlow as its backend, but experimental support
+for PyTorch has been added. To use PyTorch, the `torch` Python package must
+be installed, and PyTorch must be enabled for your trainer.
+
+#### Installing PyTorch
+
+If you've already installed ML-Agents, follow the
+[official PyTorch install instructions](https://pytorch.org/get-started/locally/) for
+your platform and configuration. Note that on Windows, you may also need Microsoft's
+[Visual C++ Redistributable](https://support.microsoft.com/en-us/help/2977003/the-latest-supported-visual-c-downloads) if you don't have it already.
+
+If you're installing or upgrading ML-Agents on Linux or Mac, you can also run
+`pip3 install mlagents[torch]` instead of `pip3 install mlagents`
+during [installation](Installation.md). On Windows, install ML-Agents first and then
+separately install PyTorch.
+
+#### Enabling PyTorch
+
+PyTorch can be enabled in one of two ways. First, by adding `--torch` to the
+`mlagents-learn` command. This will make all behaviors train with PyTorch.
+
+Second, by changing the `framework` option for your agent behavior in the
+configuration YAML as below. This will use PyTorch just for that behavior.
+
+```yaml
+behaviors:
+  YourAgentBehavior:
+    framework: pytorch
+```
--- a/docs/Training-on-Amazon-Web-Service.md
+++ b/docs/Training-on-Amazon-Web-Service.md
 2. Clone the ML-Agents repo and install the required Python packages

   ```sh
-   git clone --branch release_6 https://github.com/Unity-Technologies/ml-agents.git
+   git clone --branch release_7 https://github.com/Unity-Technologies/ml-agents.git
   cd ml-agents/ml-agents/
   pip3 install -e .
   ```
--- a/docs/Unity-Inference-Engine.md
+++ b/docs/Unity-Inference-Engine.md
 loading expects certain conventions for constants and tensor names. While it is
 possible to construct a model that follows these conventions, we don't provide
 any additional help for this. More details can be found in
-[TensorNames.cs](https://github.com/Unity-Technologies/ml-agents/blob/release_6_docs/com.unity.ml-agents/Runtime/Inference/TensorNames.cs)
+[TensorNames.cs](https://github.com/Unity-Technologies/ml-agents/blob/release_7_docs/com.unity.ml-agents/Runtime/Inference/TensorNames.cs)
-[BarracudaModelParamLoader.cs](https://github.com/Unity-Technologies/ml-agents/blob/release_6_docs/com.unity.ml-agents/Runtime/Inference/BarracudaModelParamLoader.cs).
+[BarracudaModelParamLoader.cs](https://github.com/Unity-Technologies/ml-agents/blob/release_7_docs/com.unity.ml-agents/Runtime/Inference/BarracudaModelParamLoader.cs).

 If you wish to run inference on an externally trained model, you should use
 Barracuda directly, instead of trying to run it through ML-Agents.
--- a/ml-agents-envs/mlagents_envs/env_utils.py
+++ b/ml-agents-envs/mlagents_envs/env_utils.py
        candidates = glob.glob(os.path.join(cwd, env_path + ".exe"))
        if len(candidates) == 0:
            candidates = glob.glob(env_path + ".exe")
+        if len(candidates) == 0:
+            # Look for e.g. 3DBall\UnityEnvironment.exe
+            crash_handlers = set(
+                glob.glob(os.path.join(cwd, env_path, "UnityCrashHandler*.exe"))
+            )
+            candidates = [
+                c
+                for c in glob.glob(os.path.join(cwd, env_path, "*.exe"))
+                if c not in crash_handlers
+            ]
        if len(candidates) > 0:
            launch_string = candidates[0]
    return launch_string
--- a/ml-agents/mlagents/trainers/policy/torch_policy.py
+++ b/ml-agents/mlagents/trainers/policy/torch_policy.py
        memories: Optional[torch.Tensor] = None,
        seq_len: int = 1,
        all_log_probs: bool = False,
-    ) -> Tuple[
-        torch.Tensor, torch.Tensor, torch.Tensor, Dict[str, torch.Tensor], torch.Tensor
-    ]:
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
-        dists, value_heads, memories = self.actor_critic.get_dist_and_value(
-            vec_obs, vis_obs, masks, memories, seq_len
-        )
+        if memories is None:
+            dists, memories = self.actor_critic.get_dists(
+                vec_obs, vis_obs, masks, memories, seq_len
+            )
+        else:
+            # If we're using LSTM. we need to execute the values to get the critic memories
+            dists, _, memories = self.actor_critic.get_dist_and_value(
+                vec_obs, vis_obs, masks, memories, seq_len
+            )
        action_list = self.actor_critic.sample_action(dists)
        log_probs, entropies, all_logs = ModelUtils.get_probs_and_entropy(
            action_list, dists
        else:
            actions = actions[:, 0, :]

-        return (
-            actions,
-            all_logs if all_log_probs else log_probs,
-            entropies,
-            value_heads,
-            memories,
-        )
+        return (actions, all_logs if all_log_probs else log_probs, entropies, memories)

    def evaluate_actions(
        self,

        run_out = {}
        with torch.no_grad():
-            action, log_probs, entropy, value_heads, memories = self.sample_actions(
+            action, log_probs, entropy, memories = self.sample_actions(
                vec_obs, vis_obs, masks=masks, memories=memories
            )
        run_out["action"] = ModelUtils.to_numpy(action)
        run_out["entropy"] = ModelUtils.to_numpy(entropy)
-        run_out["value_heads"] = {
-            name: ModelUtils.to_numpy(t) for name, t in value_heads.items()
-        }
-        run_out["value"] = np.mean(list(run_out["value_heads"].values()), 0)
        run_out["learning_rate"] = 0.0
        if self.use_recurrent:
            run_out["memory_out"] = ModelUtils.to_numpy(memories).squeeze(0)
--- a/ml-agents/mlagents/trainers/sac/optimizer_torch.py
+++ b/ml-agents/mlagents/trainers/sac/optimizer_torch.py
        self.target_network.q2_network.network_body.copy_normalization(
            self.policy.actor_critic.network_body
        )
-        (sampled_actions, log_probs, _, _) = self._sample_actions(
+        (sampled_actions, log_probs, _, _) = self.policy.sample_actions(
            vec_obs,
            vis_obs,
            masks=act_masks,
+        )
+        sampled_values, _ = self.policy.actor_critic.critic_pass(
+            vec_obs, vis_obs, memories, sequence_length=self.policy.sequence_length
        )
        if self.policy.use_continuous_act:
            squeezed_actions = actions.squeeze(-1)
            q2_stream = self._condense_q_streams(q2_out, actions)

        with torch.no_grad():
-            (next_actions, next_log_probs, _, _) = self._sample_actions(
+            (next_actions, next_log_probs, _, _) = self.policy.sample_actions(
                next_vec_obs,
                next_vis_obs,
                masks=act_masks,
        for reward_provider in self.reward_signals.values():
            modules.update(reward_provider.get_modules())
        return modules
-
-    def _sample_actions(
-        self,
-        vec_obs: List[torch.Tensor],
-        vis_obs: List[torch.Tensor],
-        masks: Optional[torch.Tensor] = None,
-        memories: Optional[torch.Tensor] = None,
-        seq_len: int = 1,
-        all_log_probs: bool = False,
-    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
-        """
-        :param all_log_probs: Returns (for discrete actions) a tensor of log probs, one for each action.
-        """
-        dists, memories = self.policy.actor_critic.get_dists(
-            vec_obs, vis_obs, masks, memories, seq_len
-        )
-        action_list = self.policy.actor_critic.sample_action(dists)
-        log_probs, entropies, all_logs = ModelUtils.get_probs_and_entropy(
-            action_list, dists
-        )
-        actions = torch.stack(action_list, dim=-1)
-        if self.policy.use_continuous_act:
-            actions = actions[:, :, 0]
-        else:
-            actions = actions[:, 0, :]
-
-        return (actions, all_logs if all_log_probs else log_probs, entropies, memories)
--- a/ml-agents/mlagents/trainers/tests/torch/saver/test_saver.py
+++ b/ml-agents/mlagents/trainers/tests/torch/saver/test_saver.py
    ).unsqueeze(0)

    with torch.no_grad():
-        _, log_probs1, _, _, _ = policy1.sample_actions(
+        _, log_probs1, _, _ = policy1.sample_actions(
-        _, log_probs2, _, _, _ = policy2.sample_actions(
+        _, log_probs2, _, _ = policy2.sample_actions(
            vec_obs, vis_obs, masks=masks, memories=memories, all_log_probs=True
        )

--- a/ml-agents/mlagents/trainers/tests/torch/test_policy.py
+++ b/ml-agents/mlagents/trainers/tests/torch/test_policy.py
    if len(memories) > 0:
        memories = torch.stack(memories).unsqueeze(0)

-    (
-        sampled_actions,
-        log_probs,
-        entropies,
-        sampled_values,
-        memories,
-    ) = policy.sample_actions(
+    (sampled_actions, log_probs, entropies, memories) = policy.sample_actions(
        vec_obs,
        vis_obs,
        masks=act_masks,
    else:
        assert log_probs.shape == (64, policy.behavior_spec.action_shape)
    assert entropies.shape == (64, policy.behavior_spec.action_size)
-    for val in sampled_values.values():
-        assert val.shape == (64,)

    if rnn:
        assert memories.shape == (1, 1, policy.m_size)
--- a/ml-agents/mlagents/trainers/torch/components/bc/module.py
+++ b/ml-agents/mlagents/trainers/torch/components/bc/module.py
        else:
            vis_obs = []

-        selected_actions, all_log_probs, _, _, _ = self.policy.sample_actions(
+        selected_actions, all_log_probs, _, _ = self.policy.sample_actions(
            vec_obs,
            vis_obs,
            masks=act_masks,
--- a/ml-agents/mlagents/trainers/torch/networks.py
+++ b/ml-agents/mlagents/trainers/torch/networks.py
            else 0
        )

-        self.visual_processors, self.vector_processors, encoder_input_size = ModelUtils.create_input_processors(
+        (
+            self.visual_processors,
+            self.vector_processors,
+            encoder_input_size,
+        ) = ModelUtils.create_input_processors(
            observation_shapes,
            self.h_size,
            network_settings.vis_encode_type,
--- a/utils/make_readme_table.py
+++ b/utils/make_readme_table.py
    ReleaseInfo("release_4", "1.2.0", "0.18.0", "July 15, 2020"),
    ReleaseInfo("release_5", "1.2.1", "0.18.1", "July 31, 2020"),
    ReleaseInfo("release_6", "1.3.0", "0.19.0", "August 12, 2020"),
+    ReleaseInfo("release_7", "1.4.0", "0.20.0", "September 16, 2020"),
 ]

 MAX_DAYS = 150  # do not print releases older than this many days