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using UnityEngine; using UnityEngine.UI; namespace TapSDK.UI { public class ToastPanelOpenParam : IOpenPanelParameter { public float popupTime; public string text; public ToastPanelOpenParam(string text, float popupTime) { this.text = text; this.popupTime = popupTime; } } public class ToastPanelController : BasePanelController { public Text text; public RectTransform background; public float fixVal; public string show; protected override void BindComponents() { base.BindComponents(); text = transform.Find("Root/Text").GetComponent<Text>(); background = transform.Find("Root/BGM") as RectTransform; } protected override void OnLoadSuccess() { base.OnLoadSuccess(); ToastPanelOpenParam param = this.openParam as ToastPanelOpenParam; if (param != null) { text.text = param.text; var totalLength = CalculateLengthOfText(); var x = totalLength; var y = background.sizeDelta.y; background.sizeDelta = new Vector2(x, y); this.Invoke("Close", param.popupTime); } } private float CalculateLengthOfText() { var width = text.preferredWidth + fixVal; width = Mathf.Max(200, width); width = Mathf.Min(Screen.width, width); return width; } } }
jynew/jyx2/TapSdkFiles/TapTap/Common/UI/Scripts/Base/ToastPanelController.cs/0
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import sys import os import codecs import shutil from chardet.universaldetector import UniversalDetector import chardet def getAllFiles(path, file_list): dir_list = os.listdir(path) for x in dir_list: new_x = os.path.join(path, x) if os.path.isdir(new_x): getAllFiles(new_x, file_list) else: file_tuple = os.path.splitext(new_x) if file_tuple[1] == '.cs': file_list.append(new_x) return file_list def get_encode_info(file): with open(file, 'rb') as f: data = f.read() return chardet.detect(data)['encoding'] # return charenc # with open(file, 'rb') as f: # detector = UniversalDetector() # for line in f.readlines(): # detector.feed(line) # if detector.done: # break # detector.close() # return detector.result['encoding'] if __name__ == "__main__": files = [] count = 0 path = '' if len(sys.argv) > 1 and os.path.exists(sys.argv[1]): path = sys.argv[1] else: path = "%s/../Assets/Scripts/" % os.path.dirname(os.path.realpath(sys.argv[0])) print('需要转换的路径为: ' + path) getAllFiles(path, files) for filename in files: encode_info = get_encode_info(filename) tempname = filename + "_temp" if encode_info != 'utf-8' and encode_info != 'ascii' and encode_info != None: BLOCKSIZE = 1048576 # or some other, desired size in bytes with codecs.open(filename, "r", encode_info) as sourceFile: print('file: [%s] 切换编码 [%s]-->[utf-8]' % (filename, encode_info)) with codecs.open(tempname, "w", "utf-8") as targetFile: while True: contents = sourceFile.read(BLOCKSIZE) if not contents: break targetFile.write(contents) shutil.move(tempname, filename) count += 1 print('一共处理了: [%s]个文件' % count) input('Press any key to quit program.')
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test_linux_protobuf_generation: name: Protobuf Generation Tests agent: type: Unity::VM image: ml-agents/ml-agents-ubuntu-18.04:latest flavor: b1.large variables: GRPC_VERSION: "1.14.1" CS_PROTO_PATH: "com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects" commands: - | sudo apt-get update && sudo apt-get install -y nuget eval "$($HOME/anaconda/bin/conda shell.bash hook)" conda activate python3.10 nuget install Grpc.Tools -Version $GRPC_VERSION -OutputDirectory protobuf-definitions/ python3 -m pip install --upgrade pip --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple python3 -m pip install grpcio==1.48.2 grpcio-tools==1.48.2 protobuf==3.19.6 six==1.16.0 mypy-protobuf==1.16.0 --progress-bar=off --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple pushd protobuf-definitions chmod +x Grpc.Tools.$GRPC_VERSION/tools/linux_x64/protoc chmod +x Grpc.Tools.$GRPC_VERSION/tools/linux_x64/grpc_csharp_plugin COMPILER=Grpc.Tools.$GRPC_VERSION/tools/linux_x64 ./make.sh popd mkdir -p artifacts touch artifacts/proto.patch git diff --exit-code -- :/ ":(exclude,top)$CS_PROTO_PATH/*.meta" \ || { GIT_ERR=$?; echo "protobufs need to be regenerated, apply the patch uploaded to artifacts."; \ echo "Apply the patch with the command 'git apply proto.patch'"; \ git diff -- :/ ":(exclude,top)$CS_PROTO_PATH/*.meta" > ../artifacts/proto.patch; exit $GIT_ERR; } triggers: cancel_old_ci: true expression: | (pull_request.target eq "main" OR pull_request.target eq "develop" OR pull_request.target match "release.+") AND NOT pull_request.draft AND (pull_request.changes.any match "protobuf-definitions/**" OR pull_request.changes.any match "com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/**" OR pull_request.changes.any match "ml-agents-envs/mlagents_envs/communicator_objects/**" OR pull_request.changes.any match ".yamato/protobuf-generation-test.yml") AND NOT pull_request.changes.all match "protobuf-definitions/**/*.md" artifacts: patch: paths: - "artifacts/*.*"
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{ "dependencies": { "com.unity.ai.navigation": "2.0.0", "com.unity.coding": "0.1.0-preview.13", "com.unity.inputsystem": "1.7.0", "com.unity.ml-agents": "file:../../com.unity.ml-agents", "com.unity.ml-agents.extensions": "file:../../com.unity.ml-agents.extensions", "com.unity.package-manager-doctools": "3.0.0-preview", "com.unity.package-validation-suite": "0.59.0-preview", "com.unity.test-framework": "1.3.9", "com.unity.test-framework.performance": "3.0.3", "com.unity.testtools.codecoverage": "1.2.5", "com.unity.modules.accessibility": "1.0.0", "com.unity.modules.imageconversion": "1.0.0", "com.unity.modules.physics": "1.0.0", "com.unity.modules.physics2d": "1.0.0", "com.unity.modules.ui": "1.0.0", "com.unity.modules.uielements": "1.0.0", "com.unity.modules.unityanalytics": "1.0.0" }, "registry": "https://artifactory.prd.cds.internal.unity3d.com/artifactory/api/npm/upm-candidates", "testables": [ "com.unity.ml-agents", "com.unity.ml-agents.extensions", "com.unity.inputsystem" ] }
ml-agents/DevProject/Packages/manifest.json/0
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using UnityEngine; public class HallwaySettings : MonoBehaviour { public float agentRunSpeed; public float agentRotationSpeed; public Material goalScoredMaterial; // when a goal is scored the ground will use this material for a few seconds. public Material failMaterial; // when fail, the ground will use this material for a few seconds. }
ml-agents/Project/Assets/ML-Agents/Examples/Hallway/Scripts/HallwaySettings.cs/0
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using UnityEngine; using UnityEngine.Events; public class GoalDetectTrigger : MonoBehaviour { [Header("Trigger Collider Tag To Detect")] public string tagToDetect = "goal"; //collider tag to detect [Header("Goal Value")] public float GoalValue = 1; private Collider m_col; [System.Serializable] public class TriggerEvent : UnityEvent<Collider, float> { } [Header("Trigger Callbacks")] public TriggerEvent onTriggerEnterEvent = new TriggerEvent(); public TriggerEvent onTriggerStayEvent = new TriggerEvent(); public TriggerEvent onTriggerExitEvent = new TriggerEvent(); private void OnTriggerEnter(Collider col) { if (col.CompareTag(tagToDetect)) { onTriggerEnterEvent.Invoke(m_col, GoalValue); } } private void OnTriggerStay(Collider col) { if (col.CompareTag(tagToDetect)) { onTriggerStayEvent.Invoke(m_col, GoalValue); } } private void OnTriggerExit(Collider col) { if (col.CompareTag(tagToDetect)) { onTriggerExitEvent.Invoke(m_col, GoalValue); } } // Start is called before the first frame update void Awake() { m_col = GetComponent<Collider>(); } // Update is called once per frame void Update() { } }
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using System; using System.Linq; using UnityEngine; using Random = UnityEngine.Random; using Unity.MLAgents; using Unity.MLAgents.Actuators; using Unity.MLAgents.Sensors; public class PyramidAgent : Agent { public GameObject area; PyramidArea m_MyArea; Rigidbody m_AgentRb; PyramidSwitch m_SwitchLogic; public GameObject areaSwitch; public bool useVectorObs; public override void Initialize() { m_AgentRb = GetComponent<Rigidbody>(); m_MyArea = area.GetComponent<PyramidArea>(); m_SwitchLogic = areaSwitch.GetComponent<PyramidSwitch>(); } public override void CollectObservations(VectorSensor sensor) { if (useVectorObs) { sensor.AddObservation(m_SwitchLogic.GetState()); sensor.AddObservation(transform.InverseTransformDirection(m_AgentRb.velocity)); } } public void MoveAgent(ActionSegment<int> act) { var dirToGo = Vector3.zero; var rotateDir = Vector3.zero; var action = act[0]; switch (action) { case 1: dirToGo = transform.forward * 1f; break; case 2: dirToGo = transform.forward * -1f; break; case 3: rotateDir = transform.up * 1f; break; case 4: rotateDir = transform.up * -1f; break; } transform.Rotate(rotateDir, Time.deltaTime * 200f); m_AgentRb.AddForce(dirToGo * 2f, ForceMode.VelocityChange); } public override void OnActionReceived(ActionBuffers actionBuffers) { AddReward(-1f / MaxStep); MoveAgent(actionBuffers.DiscreteActions); } public override void Heuristic(in ActionBuffers actionsOut) { var discreteActionsOut = actionsOut.DiscreteActions; if (Input.GetKey(KeyCode.D)) { discreteActionsOut[0] = 3; } else if (Input.GetKey(KeyCode.W)) { discreteActionsOut[0] = 1; } else if (Input.GetKey(KeyCode.A)) { discreteActionsOut[0] = 4; } else if (Input.GetKey(KeyCode.S)) { discreteActionsOut[0] = 2; } } public override void OnEpisodeBegin() { var enumerable = Enumerable.Range(0, 9).OrderBy(x => Guid.NewGuid()).Take(9); var items = enumerable.ToArray(); m_MyArea.CleanPyramidArea(); m_AgentRb.velocity = Vector3.zero; m_MyArea.PlaceObject(gameObject, items[0]); transform.rotation = Quaternion.Euler(new Vector3(0f, Random.Range(0, 360))); m_SwitchLogic.ResetSwitch(items[1], items[2]); m_MyArea.CreateStonePyramid(1, items[3]); m_MyArea.CreateStonePyramid(1, items[4]); m_MyArea.CreateStonePyramid(1, items[5]); m_MyArea.CreateStonePyramid(1, items[6]); m_MyArea.CreateStonePyramid(1, items[7]); m_MyArea.CreateStonePyramid(1, items[8]); } void OnCollisionEnter(Collision collision) { if (collision.gameObject.CompareTag("goal")) { SetReward(2f); EndEpisode(); } } }
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using UnityEngine; public class NumberTile : MonoBehaviour { public int NumberValue; public Material DefaultMaterial; public Material SuccessMaterial; private bool m_Visited; private MeshRenderer m_Renderer; public bool IsVisited { get { return m_Visited; } } public void VisitTile() { m_Renderer.sharedMaterial = SuccessMaterial; m_Visited = true; } public void ResetTile() { if (m_Renderer is null) { m_Renderer = GetComponentInChildren<MeshRenderer>(); } m_Renderer.sharedMaterial = DefaultMaterial; m_Visited = false; } }
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# Unity ML-Agents Toolkit Survey Your opinion matters a great deal to us. Only by hearing your thoughts on the Unity ML-Agents Toolkit can we continue to improve and grow. Please take a few minutes to let us know about it. Please email us at [[email protected]](mailto:[email protected]). <!-- [Fill out the survey](https://goo.gl/forms/qFMYSYr5TlINvG6f1) -->
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#if UNITY_2020_1_OR_NEWER using System.Collections.Generic; using UnityEngine; namespace Unity.MLAgents.Extensions.Sensors { /// <summary> /// Utility class to track a hierarchy of ArticulationBodies. /// </summary> public class ArticulationBodyPoseExtractor : PoseExtractor { ArticulationBody[] m_Bodies; public ArticulationBodyPoseExtractor(ArticulationBody rootBody) { if (rootBody == null) { return; } if (!rootBody.isRoot) { Debug.Log("Must pass ArticulationBody.isRoot"); return; } var bodies = rootBody.GetComponentsInChildren<ArticulationBody>(); if (bodies[0] != rootBody) { Debug.Log("Expected root body at index 0"); return; } var numBodies = bodies.Length; m_Bodies = bodies; int[] parentIndices = new int[numBodies]; parentIndices[0] = -1; var bodyToIndex = new Dictionary<ArticulationBody, int>(); for (var i = 0; i < numBodies; i++) { bodyToIndex[m_Bodies[i]] = i; } for (var i = 1; i < numBodies; i++) { var currentArticBody = m_Bodies[i]; // Component.GetComponentInParent will consider the provided object as well. // So start looking from the parent. var currentGameObject = currentArticBody.gameObject; var parentGameObject = currentGameObject.transform.parent; var parentArticBody = parentGameObject.GetComponentInParent<ArticulationBody>(); parentIndices[i] = bodyToIndex[parentArticBody]; } Setup(parentIndices); } /// <inheritdoc/> protected internal override Vector3 GetLinearVelocityAt(int index) { return m_Bodies[index].velocity; } /// <inheritdoc/> protected internal override Pose GetPoseAt(int index) { var body = m_Bodies[index]; var go = body.gameObject; var t = go.transform; return new Pose { rotation = t.rotation, position = t.position }; } /// <inheritdoc/> protected internal override Object GetObjectAt(int index) { return m_Bodies[index]; } internal ArticulationBody[] Bodies => m_Bodies; internal IEnumerable<ArticulationBody> GetEnabledArticulationBodies() { if (m_Bodies == null) { yield break; } for (var i = 0; i < m_Bodies.Length; i++) { var articBody = m_Bodies[i]; if (articBody == null) { // Ignore a virtual root. continue; } if (IsPoseEnabled(i)) { yield return articBody; } } } } } #endif // UNITY_2020_1_OR_NEWER
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using System.Collections.Generic; using UnityEngine; namespace Unity.MLAgents.Extensions.Sensors { /// <summary> /// Utility class to track a hierarchy of RigidBodies. These are assumed to have a root node, /// and child nodes are connect to their parents via Joints. /// </summary> public class RigidBodyPoseExtractor : PoseExtractor { Rigidbody[] m_Bodies; /// <summary> /// Optional game object used to determine the root of the poses, separate from the actual Rigidbodies /// in the hierarchy. For locomotion /// </summary> GameObject m_VirtualRoot; /// <summary> /// Initialize given a root RigidBody. /// </summary> /// <param name="rootBody">The root Rigidbody. This has no Joints on it (but other Joints may connect to it).</param> /// <param name="rootGameObject">Optional GameObject used to find Rigidbodies in the hierarchy.</param> /// <param name="virtualRoot">Optional GameObject used to determine the root of the poses, /// separate from the actual Rigidbodies in the hierarchy. For locomotion tasks, with ragdolls, this provides /// a stabilized reference frame, which can improve learning.</param> /// <param name="enableBodyPoses">Optional mapping of whether a body's psoe should be enabled or not.</param> public RigidBodyPoseExtractor(Rigidbody rootBody, GameObject rootGameObject = null, GameObject virtualRoot = null, Dictionary<Rigidbody, bool> enableBodyPoses = null) { if (rootBody == null) { return; } Rigidbody[] rbs; Joint[] joints; if (rootGameObject == null) { rbs = rootBody.GetComponentsInChildren<Rigidbody>(); joints = rootBody.GetComponentsInChildren<Joint>(); } else { rbs = rootGameObject.GetComponentsInChildren<Rigidbody>(); joints = rootGameObject.GetComponentsInChildren<Joint>(); } if (rbs == null || rbs.Length == 0) { Debug.Log("No rigid bodies found!"); return; } if (rbs[0] != rootBody) { Debug.Log("Expected root body at index 0"); return; } // Adjust the array if we have a virtual root. // This will be at index 0, and the "real" root will be parented to it. if (virtualRoot != null) { var extendedRbs = new Rigidbody[rbs.Length + 1]; for (var i = 0; i < rbs.Length; i++) { extendedRbs[i + 1] = rbs[i]; } rbs = extendedRbs; } var bodyToIndex = new Dictionary<Rigidbody, int>(rbs.Length); var parentIndices = new int[rbs.Length]; parentIndices[0] = -1; for (var i = 0; i < rbs.Length; i++) { if (rbs[i] != null) { bodyToIndex[rbs[i]] = i; } } foreach (var j in joints) { var parent = j.connectedBody; var child = j.GetComponent<Rigidbody>(); var parentIndex = bodyToIndex[parent]; var childIndex = bodyToIndex[child]; parentIndices[childIndex] = parentIndex; } if (virtualRoot != null) { // Make sure the original root treats the virtual root as its parent. parentIndices[1] = 0; m_VirtualRoot = virtualRoot; } m_Bodies = rbs; Setup(parentIndices); // By default, ignore the root SetPoseEnabled(0, false); if (enableBodyPoses != null) { foreach (var pair in enableBodyPoses) { var rb = pair.Key; if (bodyToIndex.TryGetValue(rb, out var index)) { SetPoseEnabled(index, pair.Value); } } } } /// <inheritdoc/> protected internal override Vector3 GetLinearVelocityAt(int index) { if (index == 0 && m_VirtualRoot != null) { // No velocity on the virtual root return Vector3.zero; } return m_Bodies[index].velocity; } /// <inheritdoc/> protected internal override Pose GetPoseAt(int index) { if (index == 0 && m_VirtualRoot != null) { // Use the GameObject's world transform return new Pose { rotation = m_VirtualRoot.transform.rotation, position = m_VirtualRoot.transform.position }; } var body = m_Bodies[index]; return new Pose { rotation = body.rotation, position = body.position }; } /// <inheritdoc/> protected internal override Object GetObjectAt(int index) { if (index == 0 && m_VirtualRoot != null) { return m_VirtualRoot; } return m_Bodies[index]; } internal Rigidbody[] Bodies => m_Bodies; /// <summary> /// Get a dictionary indicating which Rigidbodies' poses are enabled or disabled. /// </summary> /// <returns></returns> internal Dictionary<Rigidbody, bool> GetBodyPosesEnabled() { var bodyPosesEnabled = new Dictionary<Rigidbody, bool>(m_Bodies.Length); for (var i = 0; i < m_Bodies.Length; i++) { var rb = m_Bodies[i]; if (rb == null) { continue; // skip virtual root } bodyPosesEnabled[rb] = IsPoseEnabled(i); } return bodyPosesEnabled; } internal IEnumerable<Rigidbody> GetEnabledRigidbodies() { if (m_Bodies == null) { yield break; } for (var i = 0; i < m_Bodies.Length; i++) { var rb = m_Bodies[i]; if (rb == null) { // Ignore a virtual root. continue; } if (IsPoseEnabled(i)) { yield return rb; } } } } }
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#if UNITY_2020_1_OR_NEWER using UnityEngine; using NUnit.Framework; using Unity.MLAgents.Extensions.Sensors; namespace Unity.MLAgents.Extensions.Tests.Sensors { public class ArticulationBodyPoseExtractorTests { [TearDown] public void RemoveGameObjects() { var objects = GameObject.FindObjectsOfType<GameObject>(); foreach (var o in objects) { UnityEngine.Object.DestroyImmediate(o); } } [Test] public void TestNullRoot() { var poseExtractor = new ArticulationBodyPoseExtractor(null); // These should be no-ops poseExtractor.UpdateLocalSpacePoses(); poseExtractor.UpdateModelSpacePoses(); Assert.AreEqual(0, poseExtractor.NumPoses); } [Test] public void TestSingleBody() { var go = new GameObject(); var rootArticBody = go.AddComponent<ArticulationBody>(); var poseExtractor = new ArticulationBodyPoseExtractor(rootArticBody); Assert.AreEqual(1, poseExtractor.NumPoses); } [Test] public void TestTwoBodies() { // * rootObj // - rootArticBody // * leafGameObj // - leafArticBody var rootObj = new GameObject(); var rootArticBody = rootObj.AddComponent<ArticulationBody>(); var leafGameObj = new GameObject(); var leafArticBody = leafGameObj.AddComponent<ArticulationBody>(); leafGameObj.transform.SetParent(rootObj.transform); leafArticBody.jointType = ArticulationJointType.RevoluteJoint; var poseExtractor = new ArticulationBodyPoseExtractor(rootArticBody); Assert.AreEqual(2, poseExtractor.NumPoses); Assert.AreEqual(-1, poseExtractor.GetParentIndex(0)); Assert.AreEqual(0, poseExtractor.GetParentIndex(1)); } } } #endif
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ml-agents/com.unity.ml-agents/Runtime/Actuators/IDiscreteActionMask.cs.meta/0
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ml-agents/com.unity.ml-agents/Runtime/Areas.meta/0
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namespace Unity.MLAgents { /// <summary> /// Grouping for use in AddComponentMenu (instead of nesting the menus). /// </summary> internal enum MenuGroup { Default = 0, Sensors = 50, Actuators = 100 } }
ml-agents/com.unity.ml-agents/Runtime/Constants.cs/0
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ml-agents/com.unity.ml-agents/Runtime/EpisodeIdCounter.cs.meta/0
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ml-agents/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityInput.cs.meta/0
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ml-agents/com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/UnityToExternalGrpc.cs.meta/0
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using Unity.Sentis; using UnityEngine.Assertions; namespace Unity.MLAgents.Inference { internal static class SymbolicTensorShapeExtensions { public static long[] ToArray(this SymbolicTensorShape shape) { var shapeOut = new long[shape.rank]; // TODO investigate how critical this is and if we can just remove this assert. the alternative is to expose this again in Sentis. // Assert.IsTrue(shape.hasRank, "ValueError: Cannot convert tensor of unknown rank to TensorShape"); for (var i = 0; i < shape.rank; i++) { if (shape[i].isParam) { shapeOut[i] = 1; } else { shapeOut[i] = shape[i].value; } } return shapeOut; } } }
ml-agents/com.unity.ml-agents/Runtime/Inference/SymbolicTensorShapeExtensions.cs/0
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ml-agents/com.unity.ml-agents/Runtime/Integrations/Match3/Move.cs.meta/0
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using System.Collections.Generic; using System.Diagnostics; using Unity.MLAgents.Actuators; using Unity.MLAgents.Sensors; using Unity.MLAgents.Analytics; namespace Unity.MLAgents.Policies { /// <summary> /// The Remote Policy only works when training. /// When training your Agents, the RemotePolicy will be controlled by Python. /// </summary> internal class RemotePolicy : IPolicy { int m_AgentId; string m_FullyQualifiedBehaviorName; ActionSpec m_ActionSpec; ActionBuffers m_LastActionBuffer; bool m_AnalyticsSent; internal ICommunicator m_Communicator; /// <summary> /// List of actuators, only used for analytics /// </summary> private IList<IActuator> m_Actuators; public RemotePolicy( ActionSpec actionSpec, IList<IActuator> actuators, string fullyQualifiedBehaviorName) { m_FullyQualifiedBehaviorName = fullyQualifiedBehaviorName; m_Communicator = Academy.Instance.Communicator; m_Communicator?.SubscribeBrain(m_FullyQualifiedBehaviorName, actionSpec); m_ActionSpec = actionSpec; m_Actuators = actuators; } /// <inheritdoc /> public void RequestDecision(AgentInfo info, List<ISensor> sensors) { SendAnalytics(sensors); m_AgentId = info.episodeId; m_Communicator?.PutObservations(m_FullyQualifiedBehaviorName, info, sensors); } [Conditional("MLA_UNITY_ANALYTICS_MODULE")] void SendAnalytics(IList<ISensor> sensors) { if (!m_AnalyticsSent) { m_AnalyticsSent = true; TrainingAnalytics.RemotePolicyInitialized( m_FullyQualifiedBehaviorName, sensors, m_ActionSpec, m_Actuators ); } } /// <inheritdoc /> public ref readonly ActionBuffers DecideAction() { m_Communicator?.DecideBatch(); var actions = m_Communicator?.GetActions(m_FullyQualifiedBehaviorName, m_AgentId); m_LastActionBuffer = actions == null ? ActionBuffers.Empty : (ActionBuffers)actions; return ref m_LastActionBuffer; } public void Dispose() { } } }
ml-agents/com.unity.ml-agents/Runtime/Policies/RemotePolicy.cs/0
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namespace Unity.MLAgents.Sensors.Reflection { /// <summary> /// Sensor that wraps a boolean field or property of an object, and returns /// that as an observation. /// </summary> internal class BoolReflectionSensor : ReflectionSensorBase { public BoolReflectionSensor(ReflectionSensorInfo reflectionSensorInfo) : base(reflectionSensorInfo, 1) { } internal override void WriteReflectedField(ObservationWriter writer) { var boolVal = (System.Boolean)GetReflectedValue(); writer[0] = boolVal ? 1.0f : 0.0f; } } }
ml-agents/com.unity.ml-agents/Runtime/Sensors/Reflection/BoolReflectionSensor.cs/0
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namespace Unity.MLAgents.Sensors.Reflection { /// <summary> /// Sensor that wraps a Vector3 field or property of an object, and returns /// that as an observation. /// </summary> internal class Vector3ReflectionSensor : ReflectionSensorBase { public Vector3ReflectionSensor(ReflectionSensorInfo reflectionSensorInfo) : base(reflectionSensorInfo, 3) { } internal override void WriteReflectedField(ObservationWriter writer) { var vecVal = (UnityEngine.Vector3)GetReflectedValue(); writer.Add(vecVal); } } }
ml-agents/com.unity.ml-agents/Runtime/Sensors/Reflection/Vector3ReflectionSensor.cs/0
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using UnityEngine; namespace Unity.MLAgents.Sensors { /// <summary> /// A SensorComponent that creates a <see cref="VectorSensor"/>. /// </summary> [AddComponentMenu("ML Agents/Vector Sensor", (int)MenuGroup.Sensors)] public class VectorSensorComponent : SensorComponent { /// <summary> /// Name of the generated <see cref="VectorSensor"/> object. /// Note that changing this at runtime does not affect how the Agent sorts the sensors. /// </summary> public string SensorName { get { return m_SensorName; } set { m_SensorName = value; } } [HideInInspector, SerializeField] private string m_SensorName = "VectorSensor"; /// <summary> /// The number of float observations in the VectorSensor /// </summary> public int ObservationSize { get { return m_ObservationSize; } set { m_ObservationSize = value; } } [HideInInspector, SerializeField] int m_ObservationSize; [HideInInspector, SerializeField] ObservationType m_ObservationType; VectorSensor m_Sensor; /// <summary> /// The type of the observation. /// </summary> public ObservationType ObservationType { get { return m_ObservationType; } set { m_ObservationType = value; } } [HideInInspector, SerializeField] [Range(1, 50)] [Tooltip("Number of camera frames that will be stacked before being fed to the neural network.")] int m_ObservationStacks = 1; /// <summary> /// Whether to stack previous observations. Using 1 means no previous observations. /// Note that changing this after the sensor is created has no effect. /// </summary> public int ObservationStacks { get { return m_ObservationStacks; } set { m_ObservationStacks = value; } } /// <summary> /// Creates a VectorSensor. /// </summary> /// <returns></returns> public override ISensor[] CreateSensors() { m_Sensor = new VectorSensor(m_ObservationSize, m_SensorName, m_ObservationType); if (ObservationStacks != 1) { return new ISensor[] { new StackingSensor(m_Sensor, ObservationStacks) }; } return new ISensor[] { m_Sensor }; } /// <summary> /// Returns the underlying VectorSensor /// </summary> /// <returns></returns> public VectorSensor GetSensor() { return m_Sensor; } } }
ml-agents/com.unity.ml-agents/Runtime/Sensors/VectorSensorComponent.cs/0
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ml-agents/com.unity.ml-agents/Runtime/UnityAgentsException.cs.meta/0
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using System.Collections.Generic; using System.Linq; using NUnit.Framework; using Unity.MLAgents.Actuators; namespace Unity.MLAgents.Tests.Actuators { [TestFixture] public class ActionSpecTests { [Test] public void ActionSpecCombineTest() { var as0 = new ActionSpec(3, new[] { 3, 2, 1 }); var as1 = new ActionSpec(1, new[] { 35, 122, 1, 3, 8, 3 }); var as0NumCon = 3; var as0NumDis = as0.NumDiscreteActions; var as1NumCon = 1; var as1NumDis = as1.NumDiscreteActions; var branchSizes = new List<int>(); branchSizes.AddRange(as0.BranchSizes); branchSizes.AddRange(as1.BranchSizes); var asc = ActionSpec.Combine(as0, as1); Assert.AreEqual(as0NumCon + as1NumCon, asc.NumContinuousActions); Assert.AreEqual(as0NumDis + as1NumDis, asc.NumDiscreteActions); Assert.IsTrue(branchSizes.ToArray().SequenceEqual(asc.BranchSizes)); as0 = new ActionSpec(3); as1 = new ActionSpec(1); asc = ActionSpec.Combine(as0, as1); Assert.IsEmpty(asc.BranchSizes); } } }
ml-agents/com.unity.ml-agents/Tests/Editor/Actuators/ActionSpecTests.cs/0
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using System.Linq; using NUnit.Framework; using Unity.Mathematics; using Unity.MLAgents.Areas; using UnityEngine; namespace Unity.MLAgents.Tests.Areas { [TestFixture] public class TrainingAreaReplicatorTests { TrainingAreaReplicator m_Replicator; [SetUp] public void Setup() { var gameObject = new GameObject(); var trainingArea = new GameObject(); trainingArea.name = "MyTrainingArea"; m_Replicator = gameObject.AddComponent<TrainingAreaReplicator>(); m_Replicator.baseArea = trainingArea; } [TearDown] public void TearDown() { var trainingAreas = Resources.FindObjectsOfTypeAll<GameObject>().Where(obj => obj.name == m_Replicator.TrainingAreaName); foreach (var trainingArea in trainingAreas) { Object.DestroyImmediate(trainingArea); } m_Replicator = null; } private static object[] NumAreasCases = { new object[] {1}, new object[] {2}, new object[] {5}, new object[] {7}, new object[] {8}, new object[] {64}, new object[] {63}, }; [TestCaseSource(nameof(NumAreasCases))] public void TestComputeGridSize(int numAreas) { m_Replicator.numAreas = numAreas; m_Replicator.Awake(); m_Replicator.OnEnable(); var m_CorrectGridSize = int3.zero; var m_RootNumAreas = Mathf.Pow(numAreas, 1.0f / 3.0f); m_CorrectGridSize.x = Mathf.CeilToInt(m_RootNumAreas); m_CorrectGridSize.y = Mathf.CeilToInt(m_RootNumAreas); m_CorrectGridSize.z = Mathf.CeilToInt((float)numAreas / (m_CorrectGridSize.x * m_CorrectGridSize.y)); Assert.GreaterOrEqual(m_Replicator.GridSize.x * m_Replicator.GridSize.y * m_Replicator.GridSize.z, m_Replicator.numAreas); Assert.AreEqual(m_CorrectGridSize, m_Replicator.GridSize); } [Test] public void TestAddEnvironments() { m_Replicator.numAreas = 10; m_Replicator.buildOnly = false; m_Replicator.Awake(); m_Replicator.OnEnable(); var trainingAreas = Resources.FindObjectsOfTypeAll<GameObject>().Where(obj => obj.name == m_Replicator.TrainingAreaName); Assert.AreEqual(10, trainingAreas.Count()); } [Test] public void TestAddEnvironmentsBuildOnly() { m_Replicator.numAreas = 10; m_Replicator.buildOnly = true; m_Replicator.Awake(); m_Replicator.OnEnable(); var trainingAreas = Resources.FindObjectsOfTypeAll<GameObject>().Where(obj => obj.name == m_Replicator.TrainingAreaName); Assert.AreEqual(1, trainingAreas.Count()); } } }
ml-agents/com.unity.ml-agents/Tests/Editor/Areas/TrainingAreaReplicatorTests.cs/0
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using System.Collections.Generic; using NUnit.Framework; using Unity.Sentis; using Unity.MLAgents.Actuators; using Unity.MLAgents.Inference; namespace Unity.MLAgents.Tests { public class EditModeTestInternalBrainTensorApplier { class TestAgent : Agent { } [Test] public void Construction() { var actionSpec = new ActionSpec(); var alloc = new TensorCachingAllocator(); var mem = new Dictionary<int, List<float>>(); var tensorGenerator = new TensorApplier(actionSpec, 0, alloc, mem); Assert.IsNotNull(tensorGenerator); alloc.Dispose(); } [Test] public void ApplyContinuousActionOutput() { var actionSpec = ActionSpec.MakeContinuous(3); var inputTensor = new TensorProxy() { shape = new long[] { 2, 3 }, data = new TensorFloat(new TensorShape(2, 3), new float[] { 1, 2, 3, 4, 5, 6 }) }; var applier = new ContinuousActionOutputApplier(actionSpec); var agentIds = new List<int>() { 0, 1 }; // Dictionary from AgentId to Action var actionDict = new Dictionary<int, ActionBuffers>() { { 0, ActionBuffers.Empty }, { 1, ActionBuffers.Empty } }; applier.Apply(inputTensor, agentIds, actionDict); Assert.AreEqual(actionDict[0].ContinuousActions[0], 1); Assert.AreEqual(actionDict[0].ContinuousActions[1], 2); Assert.AreEqual(actionDict[0].ContinuousActions[2], 3); Assert.AreEqual(actionDict[1].ContinuousActions[0], 4); Assert.AreEqual(actionDict[1].ContinuousActions[1], 5); Assert.AreEqual(actionDict[1].ContinuousActions[2], 6); } [Test] public void ApplyDiscreteActionOutputLegacy() { var actionSpec = ActionSpec.MakeDiscrete(2, 3); var inputTensor = new TensorProxy() { shape = new long[] { 2, 5 }, data = new TensorFloat( new TensorShape(2, 5), new[] { 0.5f, 22.5f, 0.1f, 5f, 1f, 4f, 5f, 6f, 7f, 8f }) }; var alloc = new TensorCachingAllocator(); var applier = new LegacyDiscreteActionOutputApplier(actionSpec, 0, alloc); var agentIds = new List<int>() { 0, 1 }; // Dictionary from AgentId to Action var actionDict = new Dictionary<int, ActionBuffers>() { { 0, ActionBuffers.Empty }, { 1, ActionBuffers.Empty } }; applier.Apply(inputTensor, agentIds, actionDict); Assert.AreEqual(actionDict[0].DiscreteActions[0], 1); Assert.AreEqual(actionDict[0].DiscreteActions[1], 1); Assert.AreEqual(actionDict[1].DiscreteActions[0], 1); Assert.AreEqual(actionDict[1].DiscreteActions[1], 2); alloc.Dispose(); } [Test] public void ApplyDiscreteActionOutput() { var actionSpec = ActionSpec.MakeDiscrete(2, 3); var inputTensor = new TensorProxy() { shape = new long[] { 2, 2 }, data = new TensorInt( new TensorShape(2, 2), new[] { 1, 1, 1, 2 }), valueType = TensorProxy.TensorType.Integer }; var alloc = new TensorCachingAllocator(); var applier = new DiscreteActionOutputApplier(actionSpec, 0, alloc); var agentIds = new List<int>() { 0, 1 }; // Dictionary from AgentId to Action var actionDict = new Dictionary<int, ActionBuffers>() { { 0, ActionBuffers.Empty }, { 1, ActionBuffers.Empty } }; applier.Apply(inputTensor, agentIds, actionDict); Assert.AreEqual(actionDict[0].DiscreteActions[0], 1); Assert.AreEqual(actionDict[0].DiscreteActions[1], 1); Assert.AreEqual(actionDict[1].DiscreteActions[0], 1); Assert.AreEqual(actionDict[1].DiscreteActions[1], 2); alloc.Dispose(); } [Test] public void ApplyHybridActionOutputLegacy() { var actionSpec = new ActionSpec(3, new[] { 2, 3 }); var continuousInputTensor = new TensorProxy() { shape = new long[] { 2, 3 }, data = new TensorFloat(new TensorShape(2, 3), new float[] { 1, 2, 3, 4, 5, 6 }) }; var discreteInputTensor = new TensorProxy() { shape = new long[] { 2, 8 }, data = new TensorFloat( new TensorShape(2, 5), new[] { 0.5f, 22.5f, 0.1f, 5f, 1f, 4f, 5f, 6f, 7f, 8f }) }; var continuousApplier = new ContinuousActionOutputApplier(actionSpec); var alloc = new TensorCachingAllocator(); var discreteApplier = new LegacyDiscreteActionOutputApplier(actionSpec, 0, alloc); var agentIds = new List<int>() { 0, 1 }; // Dictionary from AgentId to Action var actionDict = new Dictionary<int, ActionBuffers>() { { 0, ActionBuffers.Empty }, { 1, ActionBuffers.Empty } }; continuousApplier.Apply(continuousInputTensor, agentIds, actionDict); discreteApplier.Apply(discreteInputTensor, agentIds, actionDict); Assert.AreEqual(actionDict[0].ContinuousActions[0], 1); Assert.AreEqual(actionDict[0].ContinuousActions[1], 2); Assert.AreEqual(actionDict[0].ContinuousActions[2], 3); Assert.AreEqual(actionDict[0].DiscreteActions[0], 1); Assert.AreEqual(actionDict[0].DiscreteActions[1], 1); Assert.AreEqual(actionDict[1].ContinuousActions[0], 4); Assert.AreEqual(actionDict[1].ContinuousActions[1], 5); Assert.AreEqual(actionDict[1].ContinuousActions[2], 6); Assert.AreEqual(actionDict[1].DiscreteActions[0], 1); Assert.AreEqual(actionDict[1].DiscreteActions[1], 2); alloc.Dispose(); } [Test] public void ApplyHybridActionOutput() { var actionSpec = new ActionSpec(3, new[] { 2, 3 }); var continuousInputTensor = new TensorProxy() { shape = new long[] { 2, 3 }, data = new TensorFloat(new TensorShape(2, 3), new float[] { 1, 2, 3, 4, 5, 6 }), valueType = TensorProxy.TensorType.FloatingPoint }; var discreteInputTensor = new TensorProxy() { shape = new long[] { 2, 2 }, data = new TensorInt( new TensorShape(2, 2), new[] { 1, 1, 1, 2 }), valueType = TensorProxy.TensorType.Integer }; var continuousApplier = new ContinuousActionOutputApplier(actionSpec); var alloc = new TensorCachingAllocator(); var discreteApplier = new DiscreteActionOutputApplier(actionSpec, 0, alloc); var agentIds = new List<int>() { 0, 1 }; // Dictionary from AgentId to Action var actionDict = new Dictionary<int, ActionBuffers>() { { 0, ActionBuffers.Empty }, { 1, ActionBuffers.Empty } }; continuousApplier.Apply(continuousInputTensor, agentIds, actionDict); discreteApplier.Apply(discreteInputTensor, agentIds, actionDict); Assert.AreEqual(actionDict[0].ContinuousActions[0], 1); Assert.AreEqual(actionDict[0].ContinuousActions[1], 2); Assert.AreEqual(actionDict[0].ContinuousActions[2], 3); Assert.AreEqual(actionDict[0].DiscreteActions[0], 1); Assert.AreEqual(actionDict[0].DiscreteActions[1], 1); Assert.AreEqual(actionDict[1].ContinuousActions[0], 4); Assert.AreEqual(actionDict[1].ContinuousActions[1], 5); Assert.AreEqual(actionDict[1].ContinuousActions[2], 6); Assert.AreEqual(actionDict[1].DiscreteActions[0], 1); Assert.AreEqual(actionDict[1].DiscreteActions[1], 2); alloc.Dispose(); } } }
ml-agents/com.unity.ml-agents/Tests/Editor/Inference/EditModeTestInternalBrainTensorApplier.cs/0
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using System.Collections.Generic; using NUnit.Framework; using Unity.MLAgents.Actuators; using Unity.MLAgents.Integrations.Match3; using UnityEngine; namespace Unity.MLAgents.Tests.Integrations.Match3 { internal class SimpleBoard : AbstractBoard { public int Rows; public int Columns; public int NumCellTypes; public int NumSpecialTypes; public int LastMoveIndex; public bool MovesAreValid = true; public bool CallbackCalled; public override BoardSize GetMaxBoardSize() { return new BoardSize { Rows = Rows, Columns = Columns, NumCellTypes = NumCellTypes, NumSpecialTypes = NumSpecialTypes }; } public override int GetCellType(int row, int col) { return 0; } public override int GetSpecialType(int row, int col) { return 0; } public override bool IsMoveValid(Move m) { return MovesAreValid; } public override bool MakeMove(Move m) { LastMoveIndex = m.MoveIndex; return MovesAreValid; } public void Callback() { CallbackCalled = true; } } public class Match3ActuatorTests { [SetUp] public void SetUp() { if (Academy.IsInitialized) { Academy.Instance.Dispose(); } } [TestCase(true)] [TestCase(false)] public void TestValidMoves(bool movesAreValid) { // Check that a board with no valid moves doesn't raise an exception. var gameObj = new GameObject(); var board = gameObj.AddComponent<SimpleBoard>(); var agent = gameObj.AddComponent<Agent>(); gameObj.AddComponent<Match3ActuatorComponent>(); board.Rows = 5; board.Columns = 5; board.NumCellTypes = 5; board.NumSpecialTypes = 0; board.MovesAreValid = movesAreValid; board.OnNoValidMovesAction = board.Callback; board.LastMoveIndex = -1; agent.LazyInitialize(); agent.RequestDecision(); Academy.Instance.EnvironmentStep(); if (movesAreValid) { Assert.IsFalse(board.CallbackCalled); } else { Assert.IsTrue(board.CallbackCalled); } Assert.AreNotEqual(-1, board.LastMoveIndex); } [Test] public void TestActionSpec() { var gameObj = new GameObject(); var board = gameObj.AddComponent<SimpleBoard>(); var actuator = gameObj.AddComponent<Match3ActuatorComponent>(); board.Rows = 5; board.Columns = 5; board.NumCellTypes = 5; board.NumSpecialTypes = 0; var actionSpec = actuator.ActionSpec; Assert.AreEqual(1, actionSpec.NumDiscreteActions); Assert.AreEqual(board.NumMoves(), actionSpec.BranchSizes[0]); } [Test] public void TestActionSpecNullBoard() { var gameObj = new GameObject(); var actuator = gameObj.AddComponent<Match3ActuatorComponent>(); var actionSpec = actuator.ActionSpec; Assert.AreEqual(0, actionSpec.NumDiscreteActions); Assert.AreEqual(0, actionSpec.NumContinuousActions); } public class HashSetActionMask : IDiscreteActionMask { public HashSet<int>[] HashSets; public HashSetActionMask(ActionSpec spec) { HashSets = new HashSet<int>[spec.NumDiscreteActions]; for (var i = 0; i < spec.NumDiscreteActions; i++) { HashSets[i] = new HashSet<int>(); } } public void SetActionEnabled(int branch, int actionIndex, bool isEnabled) { var hashSet = HashSets[branch]; if (isEnabled) { hashSet.Remove(actionIndex); } else { hashSet.Add(actionIndex); } } } [TestCase(true, TestName = "Full Board")] [TestCase(false, TestName = "Small Board")] public void TestMasking(bool fullBoard) { var gameObj = new GameObject("board"); var board = gameObj.AddComponent<StringBoard>(); var boardString = @"0105 1024 0203 2022"; board.SetBoard(boardString); var boardSize = board.GetMaxBoardSize(); if (!fullBoard) { board.CurrentRows -= 1; } var validMoves = AbstractBoardTests.GetValidMoves4x4(fullBoard, boardSize); var actuatorComponent = gameObj.AddComponent<Match3ActuatorComponent>(); var actuator = actuatorComponent.CreateActuators()[0]; var masks = new HashSetActionMask(actuator.ActionSpec); actuator.WriteDiscreteActionMask(masks); // Run through all moves and make sure those are the only valid ones HashSet<int> validIndices = new HashSet<int>(); foreach (var m in validMoves) { validIndices.Add(m.MoveIndex); } // Valid moves and masked moves should be disjoint Assert.IsFalse(validIndices.Overlaps(masks.HashSets[0])); // And they should add up to all the potential moves Assert.AreEqual(validIndices.Count + masks.HashSets[0].Count, board.NumMoves()); } [Test] public void TestNoBoardReturnsEmptyActuators() { var gameObj = new GameObject("board"); var actuatorComponent = gameObj.AddComponent<Match3ActuatorComponent>(); var actuators = actuatorComponent.CreateActuators(); Assert.AreEqual(0, actuators.Length); } } }
ml-agents/com.unity.ml-agents/Tests/Editor/Integrations/Match3/Match3ActuatorTests.cs/0
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ml-agents/com.unity.ml-agents/Tests/Editor/Policies/HeuristicPolicyTest.cs.meta/0
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using NUnit.Framework; using Unity.MLAgents.SideChannels; using UnityEngine; namespace Unity.MLAgents.Tests { public class EngineConfigurationChannelTests { float m_OldTimeScale = 1.0f; [SetUp] public void Setup() { m_OldTimeScale = Time.timeScale; } [TearDown] public void TearDown() { Time.timeScale = m_OldTimeScale; } [Test] public void TestTimeScaleClamping() { OutgoingMessage pythonMsg = new OutgoingMessage(); pythonMsg.WriteInt32((int)EngineConfigurationChannel.ConfigurationType.TimeScale); pythonMsg.WriteFloat32(1000f); var sideChannel = new EngineConfigurationChannel(); sideChannel.ProcessMessage(pythonMsg.ToByteArray()); #if UNITY_EDITOR // Should be clamped Assert.AreEqual(100.0f, Time.timeScale); #else // Not sure we can run this test from a player, but just in case, shouldn't clamp. Assert.AreEqual(1000.0f, Time.timeScale); #endif } } }
ml-agents/com.unity.ml-agents/Tests/Editor/SideChannels/EngineConfigurationChannelTests.cs/0
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behaviors: PushBlock: trainer_type: ppo hyperparameters: batch_size: 128 buffer_size: 2048 learning_rate: 0.0003 beta: 0.01 epsilon: 0.2 lambd: 0.95 num_epoch: 3 learning_rate_schedule: linear network_settings: normalize: false hidden_units: 256 num_layers: 2 vis_encode_type: simple reward_signals: extrinsic: gamma: 0.99 strength: 1.0 gail: gamma: 0.99 strength: 0.01 network_settings: normalize: false hidden_units: 128 num_layers: 2 vis_encode_type: simple learning_rate: 0.0003 use_actions: false use_vail: false demo_path: Project/Assets/ML-Agents/Examples/PushBlock/Demos/ExpertPushBlock.demo keep_checkpoints: 5 max_steps: 100000 time_horizon: 64 summary_freq: 60000 behavioral_cloning: demo_path: Project/Assets/ML-Agents/Examples/PushBlock/Demos/ExpertPushBlock.demo steps: 50000 strength: 1.0 samples_per_update: 0
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behaviors: Pyramids: trainer_type: ppo hyperparameters: batch_size: 128 buffer_size: 2048 learning_rate: 0.0003 beta: 0.01 epsilon: 0.2 lambd: 0.95 num_epoch: 3 learning_rate_schedule: linear network_settings: normalize: false hidden_units: 512 num_layers: 2 vis_encode_type: simple reward_signals: extrinsic: gamma: 0.99 strength: 1.0 curiosity: gamma: 0.99 strength: 0.02 network_settings: hidden_units: 256 learning_rate: 0.0003 keep_checkpoints: 5 max_steps: 10000000 time_horizon: 128 summary_freq: 30000
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behaviors: PushBlock: trainer_type: sac hyperparameters: learning_rate: 0.0003 learning_rate_schedule: constant batch_size: 128 buffer_size: 50000 buffer_init_steps: 0 tau: 0.005 steps_per_update: 10.0 save_replay_buffer: false init_entcoef: 0.05 reward_signal_steps_per_update: 10.0 network_settings: normalize: false hidden_units: 256 num_layers: 2 vis_encode_type: simple reward_signals: extrinsic: gamma: 0.99 strength: 1.0 keep_checkpoints: 5 max_steps: 2000000 time_horizon: 64 summary_freq: 100000
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# ML-Agents Toolkit Glossary - **Academy** - Singleton object which controls timing, reset, and training/inference settings of the environment. - **Action** - The carrying-out of a decision on the part of an agent within the environment. - **Agent** - Unity Component which produces observations and takes actions in the environment. Agents actions are determined by decisions produced by a Policy. - **Decision** - The specification produced by a Policy for an action to be carried out given an observation. - **Editor** - The Unity Editor, which may include any pane (e.g. Hierarchy, Scene, Inspector). - **Environment** - The Unity scene which contains Agents. - **Experience** - Corresponds to a tuple of [Agent observations, actions, rewards] of a single Agent obtained after a Step. - **External Coordinator** - ML-Agents class responsible for communication with outside processes (in this case, the Python API). - **FixedUpdate** - Unity method called each time the game engine is stepped. ML-Agents logic should be placed here. - **Frame** - An instance of rendering the main camera for the display. Corresponds to each `Update` call of the game engine. - **Observation** - Partial information describing the state of the environment available to a given agent. (e.g. Vector, Visual) - **Policy** - The decision making mechanism for producing decisions from observations, typically a neural network model. - **Reward** - Signal provided at every step used to indicate desirability of an agent’s action within the current state of the environment. - **State** - The underlying properties of the environment (including all agents within it) at a given time. - **Step** - Corresponds to an atomic change of the engine that happens between Agent decisions. - **Trainer** - Python class which is responsible for training a given group of Agents. - **Update** - Unity function called each time a frame is rendered. ML-Agents logic should not be placed here.
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# Unity ML-Agents Toolkit Documentation ## Installation & Set-up - [Installation](Installation.md) - [Using Virtual Environment](Using-Virtual-Environment.md) ## Getting Started - [Getting Started Guide](Getting-Started.md) - [ML-Agents Toolkit Overview](ML-Agents-Overview.md) - [Background: Unity](Background-Unity.md) - [Background: Machine Learning](Background-Machine-Learning.md) - [Background: PyTorch](Background-PyTorch.md) - [Example Environments](Learning-Environment-Examples.md) ## Creating Learning Environments - [Making a New Learning Environment](Learning-Environment-Create-New.md) - [Designing a Learning Environment](Learning-Environment-Design.md) - [Designing Agents](Learning-Environment-Design-Agents.md) - [Using an Executable Environment](Learning-Environment-Executable.md) - [ML-Agents Package Settings](Package-Settings.md) ## Training & Inference - [Training ML-Agents](Training-ML-Agents.md) - [Training Configuration File](Training-Configuration-File.md) - [Using TensorBoard to Observe Training](Using-Tensorboard.md) - [Profiling Trainers](Profiling-Python.md) - [Sentis](Sentis.md) ## Extending ML-Agents - [Creating Custom Side Channels](Custom-SideChannels.md) - [Creating Custom Samplers for Environment Parameter Randomization](Training-ML-Agents.md#defining-a-new-sampler-type) ## Hugging Face Integration - [Using Hugging Face to download and upload trained models](Hugging-Face-Integration.md) ## Python Tutorial with Google Colab - [Using a UnityEnvironment](https://colab.research.google.com/github/Unity-Technologies/ml-agents/blob/release_21_docs/colab/Colab_UnityEnvironment_1_Run.ipynb) - [Q-Learning with a UnityEnvironment](https://colab.research.google.com/github/Unity-Technologies/ml-agents/blob/release_21_docs/colab/Colab_UnityEnvironment_2_Train.ipynb) - [Using Side Channels on a UnityEnvironment](https://colab.research.google.com/github/Unity-Technologies/ml-agents/blob/release_21_docs/colab/Colab_UnityEnvironment_3_SideChannel.ipynb) ## Help - [Migrating from earlier versions of ML-Agents](Migrating.md) - [Frequently Asked Questions](FAQ.md) - [ML-Agents Glossary](Glossary.md) - [Limitations](Limitations.md) ## API Docs - [API Reference](API-Reference.md) - [Python API Documentation](Python-LLAPI-Documentation.md) - [How to use the Python API](Python-LLAPI.md) - [How to use the Unity Environment Registry](Unity-Environment-Registry.md) - [Wrapping Learning Environment as a Gym (+Baselines/Dopamine Integration)](Python-Gym-API.md) ## Translations To make the Unity ML-Agents Toolkit accessible to the global research and Unity developer communities, we're attempting to create and maintain translations of our documentation. We've started with translating a subset of the documentation to one language (Chinese), but we hope to continue translating more pages and to other languages. Consequently, we welcome any enhancements and improvements from the community. - [Chinese](../localized_docs/zh-CN/) - [Korean](../localized_docs/KR/) ## Deprecated Docs We no longer use them ourselves and so they may not be up-to-date. We've decided to keep them up just in case they are helpful to you. - [Windows Anaconda Installation](Installation-Anaconda-Windows.md) - [Using Docker](Using-Docker.md) - [Training on the Cloud with Amazon Web Services](Training-on-Amazon-Web-Service.md) - [Training on the Cloud with Microsoft Azure](Training-on-Microsoft-Azure.md) - [Using the Video Recorder](https://github.com/Unity-Technologies/video-recorder)
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# Training ML-Agents **Table of Contents** - [Training ML-Agents](#training-ml-agents) - [Training with mlagents-learn](#training-with-mlagents-learn) - [Starting Training](#starting-training) - [Observing Training](#observing-training) - [Stopping and Resuming Training](#stopping-and-resuming-training) - [Loading an Existing Model](#loading-an-existing-model) - [Training Configurations](#training-configurations) - [Adding CLI Arguments to the Training Configuration file](#adding-cli-arguments-to-the-training-configuration-file) - [Environment settings](#environment-settings) - [Engine settings](#engine-settings) - [Checkpoint settings](#checkpoint-settings) - [Torch settings:](#torch-settings) - [Behavior Configurations](#behavior-configurations) - [Default Behavior Settings](#default-behavior-settings) - [Environment Parameters](#environment-parameters) - [Environment Parameter Randomization](#environment-parameter-randomization) - [Supported Sampler Types](#supported-sampler-types) - [Training with Environment Parameter Randomization](#training-with-environment-parameter-randomization) - [Curriculum](#curriculum) - [Training with a Curriculum](#training-with-a-curriculum) - [Training Using Concurrent Unity Instances](#training-using-concurrent-unity-instances) For a broad overview of reinforcement learning, imitation learning and all the training scenarios, methods and options within the ML-Agents Toolkit, see [ML-Agents Toolkit Overview](ML-Agents-Overview.md). Once your learning environment has been created and is ready for training, the next step is to initiate a training run. Training in the ML-Agents Toolkit is powered by a dedicated Python package, `mlagents`. This package exposes a command `mlagents-learn` that is the single entry point for all training workflows (e.g. reinforcement leaning, imitation learning, curriculum learning). Its implementation can be found at [ml-agents/mlagents/trainers/learn.py](../ml-agents/mlagents/trainers/learn.py). ## Training with mlagents-learn ### Starting Training `mlagents-learn` is the main training utility provided by the ML-Agents Toolkit. It accepts a number of CLI options in addition to a YAML configuration file that contains all the configurations and hyperparameters to be used during training. The set of configurations and hyperparameters to include in this file depend on the agents in your environment and the specific training method you wish to utilize. Keep in mind that the hyperparameter values can have a big impact on the training performance (i.e. your agent's ability to learn a policy that solves the task). In this page, we will review all the hyperparameters for all training methods and provide guidelines and advice on their values. To view a description of all the CLI options accepted by `mlagents-learn`, use the `--help`: ```sh mlagents-learn --help ``` The basic command for training is: ```sh mlagents-learn <trainer-config-file> --env=<env_name> --run-id=<run-identifier> ``` where - `<trainer-config-file>` is the file path of the trainer configuration YAML. This contains all the hyperparameter values. We offer a detailed guide on the structure of this file and the meaning of the hyperparameters (and advice on how to set them) in the dedicated [Training Configurations](#training-configurations) section below. - `<env_name>`**(Optional)** is the name (including path) of your [Unity executable](Learning-Environment-Executable.md) containing the agents to be trained. If `<env_name>` is not passed, the training will happen in the Editor. Press the **Play** button in Unity when the message _"Start training by pressing the Play button in the Unity Editor"_ is displayed on the screen. - `<run-identifier>` is a unique name you can use to identify the results of your training runs. See the [Getting Started Guide](Getting-Started.md#training-a-new-model-with-reinforcement-learning) for a sample execution of the `mlagents-learn` command. #### Observing Training Regardless of which training methods, configurations or hyperparameters you provide, the training process will always generate three artifacts, all found in the `results/<run-identifier>` folder: 1. Summaries: these are training metrics that are updated throughout the training process. They are helpful to monitor your training performance and may help inform how to update your hyperparameter values. See [Using TensorBoard](Using-Tensorboard.md) for more details on how to visualize the training metrics. 1. Models: these contain the model checkpoints that are updated throughout training and the final model file (`.onnx`). This final model file is generated once either when training completes or is interrupted. 1. Timers file (under `results/<run-identifier>/run_logs`): this contains aggregated metrics on your training process, including time spent on specific code blocks. See [Profiling in Python](Profiling-Python.md) for more information on the timers generated. These artifacts are updated throughout the training process and finalized when training is completed or is interrupted. #### Stopping and Resuming Training To interrupt training and save the current progress, hit `Ctrl+C` once and wait for the model(s) to be saved out. To resume a previously interrupted or completed training run, use the `--resume` flag and make sure to specify the previously used run ID. If you would like to re-run a previously interrupted or completed training run and re-use the same run ID (in this case, overwriting the previously generated artifacts), then use the `--force` flag. #### Loading an Existing Model You can also use this mode to run inference of an already-trained model in Python by using both the `--resume` and `--inference` flags. Note that if you want to run inference in Unity, you should use the [Sentis](Getting-Started.md#running-a-pre-trained-model). Additionally, if the network architecture changes, you may still load an existing model, but ML-Agents will only load the parts of the model it can load and ignore all others. For instance, if you add a new reward signal, the existing model will load but the new reward signal will be initialized from scratch. If you have a model with a visual encoder (CNN) but change the `hidden_units`, the CNN will be loaded but the body of the network will be initialized from scratch. Alternatively, you might want to start a new training run but _initialize_ it using an already-trained model. You may want to do this, for instance, if your environment changed and you want a new model, but the old behavior is still better than random. You can do this by specifying `--initialize-from=<run-identifier>`, where `<run-identifier>` is the old run ID. ## Training Configurations The Unity ML-Agents Toolkit provides a wide range of training scenarios, methods and options. As such, specific training runs may require different training configurations and may generate different artifacts and TensorBoard statistics. This section offers a detailed guide into how to manage the different training set-ups withing the toolkit. More specifically, this section offers a detailed guide on the command-line flags for `mlagents-learn` that control the training configurations: - `<trainer-config-file>`: defines the training hyperparameters for each Behavior in the scene, and the set-ups for the environment parameters (Curriculum Learning and Environment Parameter Randomization) It is important to highlight that successfully training a Behavior in the ML-Agents Toolkit involves tuning the training hyperparameters and configuration. This guide contains some best practices for tuning the training process when the default parameters don't seem to be giving the level of performance you would like. We provide sample configuration files for our example environments in the [config/](../config/) directory. The `config/ppo/3DBall.yaml` was used to train the 3D Balance Ball in the [Getting Started](Getting-Started.md) guide. That configuration file uses the PPO trainer, but we also have configuration files for SAC and GAIL. Additionally, the set of configurations you provide depend on the training functionalities you use (see [ML-Agents Toolkit Overview](ML-Agents-Overview.md) for a description of all the training functionalities). Each functionality you add typically has its own training configurations. For instance: - Use PPO or SAC? - Use Recurrent Neural Networks for adding memory to your agents? - Use the intrinsic curiosity module? - Ignore the environment reward signal? - Pre-train using behavioral cloning? (Assuming you have recorded demonstrations.) - Include the GAIL intrinsic reward signals? (Assuming you have recorded demonstrations.) - Use self-play? (Assuming your environment includes multiple agents.) The trainer config file, `<trainer-config-file>`, determines the features you will use during training, and the answers to the above questions will dictate its contents. The rest of this guide breaks down the different sub-sections of the trainer config file and explains the possible settings for each. If you need a list of all the trainer configurations, please see [Training Configuration File](Training-Configuration-File.md). **NOTE:** The configuration file format has been changed between 0.17.0 and 0.18.0 and between 0.18.0 and onwards. To convert an old set of configuration files (trainer config, curriculum, and sampler files) to the new format, a script has been provided. Run `python -m mlagents.trainers.upgrade_config -h` in your console to see the script's usage. ### Adding CLI Arguments to the Training Configuration file Additionally, within the training configuration YAML file, you can also add the CLI arguments (such as `--num-envs`). Reminder that a detailed description of all the CLI arguments can be found by using the help utility: ```sh mlagents-learn --help ``` These additional CLI arguments are grouped into environment, engine, checkpoint and torch. The available settings and example values are shown below. #### Environment settings ```yaml env_settings: env_path: FoodCollector env_args: null base_port: 5005 num_envs: 1 timeout_wait: 10 seed: -1 max_lifetime_restarts: 10 restarts_rate_limit_n: 1 restarts_rate_limit_period_s: 60 ``` #### Engine settings ```yaml engine_settings: width: 84 height: 84 quality_level: 5 time_scale: 20 target_frame_rate: -1 capture_frame_rate: 60 no_graphics: false ``` #### Checkpoint settings ```yaml checkpoint_settings: run_id: foodtorch initialize_from: null load_model: false resume: false force: true train_model: false inference: false ``` #### Torch settings: ```yaml torch_settings: device: cpu ``` ### Behavior Configurations The primary section of the trainer config file is a set of configurations for each Behavior in your scene. These are defined under the sub-section `behaviors` in your trainer config file. Some of the configurations are required while others are optional. To help us get started, below is a sample file that includes all the possible settings if we're using a PPO trainer with all the possible training functionalities enabled (memory, behavioral cloning, curiosity, GAIL and self-play). You will notice that curriculum and environment parameter randomization settings are not part of the `behaviors` configuration, but in their own section called `environment_parameters`. ```yaml behaviors: BehaviorPPO: trainer_type: ppo hyperparameters: # Hyperparameters common to PPO and SAC batch_size: 1024 buffer_size: 10240 learning_rate: 3.0e-4 learning_rate_schedule: linear # PPO-specific hyperparameters beta: 5.0e-3 beta_schedule: constant epsilon: 0.2 epsilon_schedule: linear lambd: 0.95 num_epoch: 3 shared_critic: False # Configuration of the neural network (common to PPO/SAC) network_settings: vis_encode_type: simple normalize: false hidden_units: 128 num_layers: 2 # memory memory: sequence_length: 64 memory_size: 256 # Trainer configurations common to all trainers max_steps: 5.0e5 time_horizon: 64 summary_freq: 10000 keep_checkpoints: 5 checkpoint_interval: 50000 threaded: false init_path: null # behavior cloning behavioral_cloning: demo_path: Project/Assets/ML-Agents/Examples/Pyramids/Demos/ExpertPyramid.demo strength: 0.5 steps: 150000 batch_size: 512 num_epoch: 3 samples_per_update: 0 reward_signals: # environment reward (default) extrinsic: strength: 1.0 gamma: 0.99 # curiosity module curiosity: strength: 0.02 gamma: 0.99 encoding_size: 256 learning_rate: 3.0e-4 # GAIL gail: strength: 0.01 gamma: 0.99 encoding_size: 128 demo_path: Project/Assets/ML-Agents/Examples/Pyramids/Demos/ExpertPyramid.demo learning_rate: 3.0e-4 use_actions: false use_vail: false # self-play self_play: window: 10 play_against_latest_model_ratio: 0.5 save_steps: 50000 swap_steps: 2000 team_change: 100000 ``` Here is an equivalent file if we use an SAC trainer instead. Notice that the configurations for the additional functionalities (memory, behavioral cloning, curiosity and self-play) remain unchanged. ```yaml behaviors: BehaviorSAC: trainer_type: sac # Trainer configs common to PPO/SAC (excluding reward signals) # same as PPO config # SAC-specific configs (replaces the hyperparameters section above) hyperparameters: # Hyperparameters common to PPO and SAC # Same as PPO config # SAC-specific hyperparameters # Replaces the "PPO-specific hyperparameters" section above buffer_init_steps: 0 tau: 0.005 steps_per_update: 10.0 save_replay_buffer: false init_entcoef: 0.5 reward_signal_steps_per_update: 10.0 # Configuration of the neural network (common to PPO/SAC) network_settings: # Same as PPO config # Trainer configurations common to all trainers # <Same as PPO config> # pre-training using behavior cloning behavioral_cloning: # same as PPO config reward_signals: # environment reward extrinsic: # same as PPO config # curiosity module curiosity: # same as PPO config # GAIL gail: # same as PPO config # self-play self_play: # same as PPO config ``` We now break apart the components of the configuration file and describe what each of these parameters mean and provide guidelines on how to set them. See [Training Configuration File](Training-Configuration-File.md) for a detailed description of all the configurations listed above, along with their defaults. Unless otherwise specified, omitting a configuration will revert it to its default. ### Default Behavior Settings In some cases, you may want to specify a set of default configurations for your Behaviors. This may be useful, for instance, if your Behavior names are generated procedurally by the environment and not known before runtime, or if you have many Behaviors with very similar settings. To specify a default configuration, insert a `default_settings` section in your YAML. This section should be formatted exactly like a configuration for a Behavior. ```yaml default_settings: # < Same as Behavior configuration > behaviors: # < Same as above > ``` Behaviors found in the environment that aren't specified in the YAML will now use the `default_settings`, and unspecified settings in behavior configurations will default to the values in `default_settings` if specified there. ### Environment Parameters In order to control the `EnvironmentParameters` in the Unity simulation during training, you need to add a section called `environment_parameters`. For example you can set the value of an `EnvironmentParameter` called `my_environment_parameter` to `3.0` with the following code : ```yml behaviors: BehaviorY: # < Same as above > # Add this section environment_parameters: my_environment_parameter: 3.0 ``` Inside the Unity simulation, you can access your Environment Parameters by doing : ```csharp Academy.Instance.EnvironmentParameters.GetWithDefault("my_environment_parameter", 0.0f); ``` #### Environment Parameter Randomization To enable environment parameter randomization, you need to edit the `environment_parameters` section of your training configuration yaml file. Instead of providing a single float value for your environment parameter, you can specify a sampler instead. Here is an example with three environment parameters called `mass`, `length` and `scale`: ```yml behaviors: BehaviorY: # < Same as above > # Add this section environment_parameters: mass: sampler_type: uniform sampler_parameters: min_value: 0.5 max_value: 10 length: sampler_type: multirangeuniform sampler_parameters: intervals: [[7, 10], [15, 20]] scale: sampler_type: gaussian sampler_parameters: mean: 2 st_dev: .3 ``` | **Setting** | **Description** | | :--------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `sampler_type` | A string identifier for the type of sampler to use for this `Environment Parameter`. | | `sampler_parameters` | The parameters for a given `sampler_type`. Samplers of different types can have different `sampler_parameters` | ##### Supported Sampler Types Below is a list of the `sampler_type` values supported by the toolkit. - `uniform` - Uniform sampler - Uniformly samples a single float value from a range with a given minimum and maximum value (inclusive). - **parameters** - `min_value`, `max_value` - `gaussian` - Gaussian sampler - Samples a single float value from a normal distribution with a given mean and standard deviation. - **parameters** - `mean`, `st_dev` - `multirange_uniform` - Multirange uniform sampler - First, samples an interval from a set of intervals in proportion to relative length of the intervals. Then, uniformly samples a single float value from the sampled interval (inclusive). This sampler can take an arbitrary number of intervals in a list in the following format: [[`interval_1_min`, `interval_1_max`], [`interval_2_min`, `interval_2_max`], ...] - **parameters** - `intervals` The implementation of the samplers can be found in the [Samplers.cs file](https://github.com/Unity-Technologies/ml-agents/blob/main/com.unity.ml-agents/Runtime/Sampler.cs). ##### Training with Environment Parameter Randomization After the sampler configuration is defined, we proceed by launching `mlagents-learn` and specify trainer configuration with parameter randomization enabled. For example, if we wanted to train the 3D ball agent with parameter randomization, we would run ```sh mlagents-learn config/ppo/3DBall_randomize.yaml --run-id=3D-Ball-randomize ``` We can observe progress and metrics via TensorBoard. #### Curriculum To enable curriculum learning, you need to add a `curriculum` sub-section to your environment parameter. Here is one example with the environment parameter `my_environment_parameter` : ```yml behaviors: BehaviorY: # < Same as above > # Add this section environment_parameters: my_environment_parameter: curriculum: - name: MyFirstLesson # The '-' is important as this is a list completion_criteria: measure: progress behavior: my_behavior signal_smoothing: true min_lesson_length: 100 threshold: 0.2 value: 0.0 - name: MySecondLesson # This is the start of the second lesson completion_criteria: measure: progress behavior: my_behavior signal_smoothing: true min_lesson_length: 100 threshold: 0.6 require_reset: true value: sampler_type: uniform sampler_parameters: min_value: 4.0 max_value: 7.0 - name: MyLastLesson value: 8.0 ``` Note that this curriculum __only__ applies to `my_environment_parameter`. The `curriculum` section contains a list of `Lessons`. In the example, the lessons are named `MyFirstLesson`, `MySecondLesson` and `MyLastLesson`. Each `Lesson` has 3 fields : - `name` which is a user defined name for the lesson (The name of the lesson will be displayed in the console when the lesson changes) - `completion_criteria` which determines what needs to happen in the simulation before the lesson can be considered complete. When that condition is met, the curriculum moves on to the next `Lesson`. Note that you do not need to specify a `completion_criteria` for the last `Lesson` - `value` which is the value the environment parameter will take during the lesson. Note that this can be a float or a sampler. There are the different settings of the `completion_criteria` : | **Setting** | **Description** | | :------------------ | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `measure` | What to measure learning progress, and advancement in lessons by.<br><br> `reward` uses a measure of received reward, `progress` uses the ratio of steps/max_steps, while `Elo` is available only for self-play situations and uses Elo score as a curriculum completion measure. | | `behavior` | Specifies which behavior is being tracked. There can be multiple behaviors with different names, each at different points of training. This setting allows the curriculum to track only one of them. | | `threshold` | Determines at what point in value of `measure` the lesson should be increased. | | `min_lesson_length` | The minimum number of episodes that should be completed before the lesson can change. If `measure` is set to `reward`, the average cumulative reward of the last `min_lesson_length` episodes will be used to determine if the lesson should change. Must be nonnegative. <br><br> **Important**: the average reward that is compared to the thresholds is different than the mean reward that is logged to the console. For example, if `min_lesson_length` is `100`, the lesson will increment after the average cumulative reward of the last `100` episodes exceeds the current threshold. The mean reward logged to the console is dictated by the `summary_freq` parameter defined above. | | `signal_smoothing` | Whether to weight the current progress measure by previous values. | | `require_reset` | Whether changing lesson requires the environment to reset (default: false) | ##### Training with a Curriculum Once we have specified our metacurriculum and curricula, we can launch `mlagents-learn` to point to the config file containing our curricula and PPO will train using Curriculum Learning. For example, to train agents in the Wall Jump environment with curriculum learning, we can run: ```sh mlagents-learn config/ppo/WallJump_curriculum.yaml --run-id=wall-jump-curriculum ``` We can then keep track of the current lessons and progresses via TensorBoard. If you've terminated the run, you can resume it using `--resume` and lesson progress will start off where it ended. ### Training Using Concurrent Unity Instances In order to run concurrent Unity instances during training, set the number of environment instances using the command line option `--num-envs=<n>` when you invoke `mlagents-learn`. Optionally, you can also set the `--base-port`, which is the starting port used for the concurrent Unity instances. Some considerations: - **Buffer Size** - If you are having trouble getting an agent to train, even with multiple concurrent Unity instances, you could increase `buffer_size` in the trainer config file. A common practice is to multiply `buffer_size` by `num-envs`. - **Resource Constraints** - Invoking concurrent Unity instances is constrained by the resources on the machine. Please use discretion when setting `--num-envs=<n>`. - **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.
ml-agents/docs/Training-ML-Agents.md/0
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ml-agents/docs/doxygen/header.html/0
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# @generated by generate_proto_mypy_stubs.py. Do not edit! import sys from google.protobuf.descriptor import ( Descriptor as google___protobuf___descriptor___Descriptor, ) from google.protobuf.internal.containers import ( RepeatedScalarFieldContainer as google___protobuf___internal___containers___RepeatedScalarFieldContainer, ) from google.protobuf.message import ( Message as google___protobuf___message___Message, ) from typing import ( Iterable as typing___Iterable, Optional as typing___Optional, ) from typing_extensions import ( Literal as typing_extensions___Literal, ) builtin___bool = bool builtin___bytes = bytes builtin___float = float builtin___int = int class AgentActionProto(google___protobuf___message___Message): DESCRIPTOR: google___protobuf___descriptor___Descriptor = ... vector_actions_deprecated = ... # type: google___protobuf___internal___containers___RepeatedScalarFieldContainer[builtin___float] value = ... # type: builtin___float continuous_actions = ... # type: google___protobuf___internal___containers___RepeatedScalarFieldContainer[builtin___float] discrete_actions = ... # type: google___protobuf___internal___containers___RepeatedScalarFieldContainer[builtin___int] def __init__(self, *, vector_actions_deprecated : typing___Optional[typing___Iterable[builtin___float]] = None, value : typing___Optional[builtin___float] = None, continuous_actions : typing___Optional[typing___Iterable[builtin___float]] = None, discrete_actions : typing___Optional[typing___Iterable[builtin___int]] = None, ) -> None: ... @classmethod def FromString(cls, s: builtin___bytes) -> AgentActionProto: ... def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ... def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ... if sys.version_info >= (3,): def ClearField(self, field_name: typing_extensions___Literal[u"continuous_actions",u"discrete_actions",u"value",u"vector_actions_deprecated"]) -> None: ... else: def ClearField(self, field_name: typing_extensions___Literal[u"continuous_actions",b"continuous_actions",u"discrete_actions",b"discrete_actions",u"value",b"value",u"vector_actions_deprecated",b"vector_actions_deprecated"]) -> None: ...
ml-agents/ml-agents-envs/mlagents_envs/communicator_objects/agent_action_pb2.pyi/0
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# @generated by generate_proto_mypy_stubs.py. Do not edit! import sys from google.protobuf.descriptor import ( Descriptor as google___protobuf___descriptor___Descriptor, ) from google.protobuf.message import ( Message as google___protobuf___message___Message, ) from typing import ( Optional as typing___Optional, ) from typing_extensions import ( Literal as typing_extensions___Literal, ) builtin___bool = bool builtin___bytes = bytes builtin___float = float builtin___int = int class EngineConfigurationProto(google___protobuf___message___Message): DESCRIPTOR: google___protobuf___descriptor___Descriptor = ... width = ... # type: builtin___int height = ... # type: builtin___int quality_level = ... # type: builtin___int time_scale = ... # type: builtin___float target_frame_rate = ... # type: builtin___int show_monitor = ... # type: builtin___bool def __init__(self, *, width : typing___Optional[builtin___int] = None, height : typing___Optional[builtin___int] = None, quality_level : typing___Optional[builtin___int] = None, time_scale : typing___Optional[builtin___float] = None, target_frame_rate : typing___Optional[builtin___int] = None, show_monitor : typing___Optional[builtin___bool] = None, ) -> None: ... @classmethod def FromString(cls, s: builtin___bytes) -> EngineConfigurationProto: ... def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ... def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ... if sys.version_info >= (3,): def ClearField(self, field_name: typing_extensions___Literal[u"height",u"quality_level",u"show_monitor",u"target_frame_rate",u"time_scale",u"width"]) -> None: ... else: def ClearField(self, field_name: typing_extensions___Literal[u"height",b"height",u"quality_level",b"quality_level",u"show_monitor",b"show_monitor",u"target_frame_rate",b"target_frame_rate",u"time_scale",b"time_scale",u"width",b"width"]) -> None: ...
ml-agents/ml-agents-envs/mlagents_envs/communicator_objects/engine_configuration_pb2.pyi/0
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# @generated by generate_proto_mypy_stubs.py. Do not edit! import sys from google.protobuf.descriptor import ( Descriptor as google___protobuf___descriptor___Descriptor, ) from google.protobuf.message import ( Message as google___protobuf___message___Message, ) from mlagents_envs.communicator_objects.capabilities_pb2 import ( UnityRLCapabilitiesProto as mlagents_envs___communicator_objects___capabilities_pb2___UnityRLCapabilitiesProto, ) from typing import ( Optional as typing___Optional, Text as typing___Text, ) from typing_extensions import ( Literal as typing_extensions___Literal, ) builtin___bool = bool builtin___bytes = bytes builtin___float = float builtin___int = int class UnityRLInitializationInputProto(google___protobuf___message___Message): DESCRIPTOR: google___protobuf___descriptor___Descriptor = ... seed = ... # type: builtin___int communication_version = ... # type: typing___Text package_version = ... # type: typing___Text num_areas = ... # type: builtin___int @property def capabilities(self) -> mlagents_envs___communicator_objects___capabilities_pb2___UnityRLCapabilitiesProto: ... def __init__(self, *, seed : typing___Optional[builtin___int] = None, communication_version : typing___Optional[typing___Text] = None, package_version : typing___Optional[typing___Text] = None, capabilities : typing___Optional[mlagents_envs___communicator_objects___capabilities_pb2___UnityRLCapabilitiesProto] = None, num_areas : typing___Optional[builtin___int] = None, ) -> None: ... @classmethod def FromString(cls, s: builtin___bytes) -> UnityRLInitializationInputProto: ... def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ... def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ... if sys.version_info >= (3,): def HasField(self, field_name: typing_extensions___Literal[u"capabilities"]) -> builtin___bool: ... def ClearField(self, field_name: typing_extensions___Literal[u"capabilities",u"communication_version",u"num_areas",u"package_version",u"seed"]) -> None: ... else: def HasField(self, field_name: typing_extensions___Literal[u"capabilities",b"capabilities"]) -> builtin___bool: ... def ClearField(self, field_name: typing_extensions___Literal[u"capabilities",b"capabilities",u"communication_version",b"communication_version",u"num_areas",b"num_areas",u"package_version",b"package_version",u"seed",b"seed"]) -> None: ...
ml-agents/ml-agents-envs/mlagents_envs/communicator_objects/unity_rl_initialization_input_pb2.pyi/0
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import itertools import numpy as np from typing import Any, Dict, List, Optional, Tuple, Union import gym from gym import error, spaces from mlagents_envs.base_env import ActionTuple, BaseEnv from mlagents_envs.base_env import DecisionSteps, TerminalSteps from mlagents_envs import logging_util class UnityGymException(error.Error): """ Any error related to the gym wrapper of ml-agents. """ pass logger = logging_util.get_logger(__name__) GymStepResult = Tuple[np.ndarray, float, bool, Dict] class UnityToGymWrapper(gym.Env): """ Provides Gym wrapper for Unity Learning Environments. """ def __init__( self, unity_env: BaseEnv, uint8_visual: bool = False, flatten_branched: bool = False, allow_multiple_obs: bool = False, action_space_seed: Optional[int] = None, ): """ Environment initialization :param unity_env: The Unity BaseEnv to be wrapped in the gym. Will be closed when the UnityToGymWrapper closes. :param uint8_visual: Return visual observations as uint8 (0-255) matrices instead of float (0.0-1.0). :param flatten_branched: If True, turn branched discrete action spaces into a Discrete space rather than MultiDiscrete. :param allow_multiple_obs: If True, return a list of np.ndarrays as observations with the first elements containing the visual observations and the last element containing the array of vector observations. If False, returns a single np.ndarray containing either only a single visual observation or the array of vector observations. :param action_space_seed: If non-None, will be used to set the random seed on created gym.Space instances. """ self._env = unity_env # Take a single step so that the brain information will be sent over if not self._env.behavior_specs: self._env.step() self.visual_obs = None # Save the step result from the last time all Agents requested decisions. self._previous_decision_step: Optional[DecisionSteps] = None self._flattener = None # Hidden flag used by Atari environments to determine if the game is over self.game_over = False self._allow_multiple_obs = allow_multiple_obs # Check brain configuration if len(self._env.behavior_specs) != 1: raise UnityGymException( "There can only be one behavior in a UnityEnvironment " "if it is wrapped in a gym." ) self.name = list(self._env.behavior_specs.keys())[0] self.group_spec = self._env.behavior_specs[self.name] if self._get_n_vis_obs() == 0 and self._get_vec_obs_size() == 0: raise UnityGymException( "There are no observations provided by the environment." ) if not self._get_n_vis_obs() >= 1 and uint8_visual: logger.warning( "uint8_visual was set to true, but visual observations are not in use. " "This setting will not have any effect." ) else: self.uint8_visual = uint8_visual if ( self._get_n_vis_obs() + self._get_vec_obs_size() >= 2 and not self._allow_multiple_obs ): logger.warning( "The environment contains multiple observations. " "You must define allow_multiple_obs=True to receive them all. " "Otherwise, only the first visual observation (or vector observation if" "there are no visual observations) will be provided in the observation." ) # Check for number of agents in scene. self._env.reset() decision_steps, _ = self._env.get_steps(self.name) self._check_agents(len(decision_steps)) self._previous_decision_step = decision_steps # Set action spaces if self.group_spec.action_spec.is_discrete(): self.action_size = self.group_spec.action_spec.discrete_size branches = self.group_spec.action_spec.discrete_branches if self.group_spec.action_spec.discrete_size == 1: self._action_space = spaces.Discrete(branches[0]) else: if flatten_branched: self._flattener = ActionFlattener(branches) self._action_space = self._flattener.action_space else: self._action_space = spaces.MultiDiscrete(branches) elif self.group_spec.action_spec.is_continuous(): if flatten_branched: logger.warning( "The environment has a non-discrete action space. It will " "not be flattened." ) self.action_size = self.group_spec.action_spec.continuous_size high = np.array([1] * self.group_spec.action_spec.continuous_size) self._action_space = spaces.Box(-high, high, dtype=np.float32) else: raise UnityGymException( "The gym wrapper does not provide explicit support for both discrete " "and continuous actions." ) if action_space_seed is not None: self._action_space.seed(action_space_seed) # Set observations space list_spaces: List[gym.Space] = [] shapes = self._get_vis_obs_shape() for shape in shapes: if uint8_visual: list_spaces.append(spaces.Box(0, 255, dtype=np.uint8, shape=shape)) else: list_spaces.append(spaces.Box(0, 1, dtype=np.float32, shape=shape)) if self._get_vec_obs_size() > 0: # vector observation is last high = np.array([np.inf] * self._get_vec_obs_size()) list_spaces.append(spaces.Box(-high, high, dtype=np.float32)) if self._allow_multiple_obs: self._observation_space = spaces.Tuple(list_spaces) else: self._observation_space = list_spaces[0] # only return the first one def reset(self) -> Union[List[np.ndarray], np.ndarray]: """Resets the state of the environment and returns an initial observation. Returns: observation (object/list): the initial observation of the space. """ self._env.reset() decision_step, _ = self._env.get_steps(self.name) n_agents = len(decision_step) self._check_agents(n_agents) self.game_over = False res: GymStepResult = self._single_step(decision_step) return res[0] def step(self, action: List[Any]) -> GymStepResult: """Run one timestep of the environment's dynamics. When end of episode is reached, you are responsible for calling `reset()` to reset this environment's state. Accepts an action and returns a tuple (observation, reward, done, info). Args: action (object/list): an action provided by the environment Returns: observation (object/list): agent's observation of the current environment reward (float/list) : amount of reward returned after previous action done (boolean/list): whether the episode has ended. info (dict): contains auxiliary diagnostic information. """ if self.game_over: raise UnityGymException( "You are calling 'step()' even though this environment has already " "returned done = True. You must always call 'reset()' once you " "receive 'done = True'." ) if self._flattener is not None: # Translate action into list action = self._flattener.lookup_action(action) action = np.array(action).reshape((1, self.action_size)) action_tuple = ActionTuple() if self.group_spec.action_spec.is_continuous(): action_tuple.add_continuous(action) else: action_tuple.add_discrete(action) self._env.set_actions(self.name, action_tuple) self._env.step() decision_step, terminal_step = self._env.get_steps(self.name) self._check_agents(max(len(decision_step), len(terminal_step))) if len(terminal_step) != 0: # The agent is done self.game_over = True return self._single_step(terminal_step) else: return self._single_step(decision_step) def _single_step(self, info: Union[DecisionSteps, TerminalSteps]) -> GymStepResult: if self._allow_multiple_obs: visual_obs = self._get_vis_obs_list(info) visual_obs_list = [] for obs in visual_obs: visual_obs_list.append(self._preprocess_single(obs[0])) default_observation = visual_obs_list if self._get_vec_obs_size() >= 1: default_observation.append(self._get_vector_obs(info)[0, :]) else: if self._get_n_vis_obs() >= 1: visual_obs = self._get_vis_obs_list(info) default_observation = self._preprocess_single(visual_obs[0][0]) else: default_observation = self._get_vector_obs(info)[0, :] if self._get_n_vis_obs() >= 1: visual_obs = self._get_vis_obs_list(info) self.visual_obs = self._preprocess_single(visual_obs[0][0]) done = isinstance(info, TerminalSteps) return (default_observation, info.reward[0], done, {"step": info}) def _preprocess_single(self, single_visual_obs: np.ndarray) -> np.ndarray: if self.uint8_visual: return (255.0 * single_visual_obs).astype(np.uint8) else: return single_visual_obs def _get_n_vis_obs(self) -> int: result = 0 for obs_spec in self.group_spec.observation_specs: if len(obs_spec.shape) == 3: result += 1 return result def _get_vis_obs_shape(self) -> List[Tuple]: result: List[Tuple] = [] for obs_spec in self.group_spec.observation_specs: if len(obs_spec.shape) == 3: result.append(obs_spec.shape) return result def _get_vis_obs_list( self, step_result: Union[DecisionSteps, TerminalSteps] ) -> List[np.ndarray]: result: List[np.ndarray] = [] for obs in step_result.obs: if len(obs.shape) == 4: result.append(obs) return result def _get_vector_obs( self, step_result: Union[DecisionSteps, TerminalSteps] ) -> np.ndarray: result: List[np.ndarray] = [] for obs in step_result.obs: if len(obs.shape) == 2: result.append(obs) return np.concatenate(result, axis=1) def _get_vec_obs_size(self) -> int: result = 0 for obs_spec in self.group_spec.observation_specs: if len(obs_spec.shape) == 1: result += obs_spec.shape[0] return result def render(self, mode="rgb_array"): """ Return the latest visual observations. Note that it will not render a new frame of the environment. """ return self.visual_obs def close(self) -> None: """Override _close in your subclass to perform any necessary cleanup. Environments will automatically close() themselves when garbage collected or when the program exits. """ self._env.close() def seed(self, seed: Any = None) -> None: """Sets the seed for this env's random number generator(s). Currently not implemented. """ logger.warning("Could not seed environment %s", self.name) return @staticmethod def _check_agents(n_agents: int) -> None: if n_agents > 1: raise UnityGymException( f"There can only be one Agent in the environment but {n_agents} were detected." ) @property def metadata(self): return {"render.modes": ["rgb_array"]} @property def reward_range(self) -> Tuple[float, float]: return -float("inf"), float("inf") @property def action_space(self) -> gym.Space: return self._action_space @property def observation_space(self): return self._observation_space class ActionFlattener: """ Flattens branched discrete action spaces into single-branch discrete action spaces. """ def __init__(self, branched_action_space): """ Initialize the flattener. :param branched_action_space: A List containing the sizes of each branch of the action space, e.g. [2,3,3] for three branches with size 2, 3, and 3 respectively. """ self._action_shape = branched_action_space self.action_lookup = self._create_lookup(self._action_shape) self.action_space = spaces.Discrete(len(self.action_lookup)) @classmethod def _create_lookup(self, branched_action_space): """ Creates a Dict that maps discrete actions (scalars) to branched actions (lists). Each key in the Dict maps to one unique set of branched actions, and each value contains the List of branched actions. """ possible_vals = [range(_num) for _num in branched_action_space] all_actions = [list(_action) for _action in itertools.product(*possible_vals)] # Dict should be faster than List for large action spaces action_lookup = { _scalar: _action for (_scalar, _action) in enumerate(all_actions) } return action_lookup def lookup_action(self, action): """ Convert a scalar discrete action into a unique set of branched actions. :param action: A scalar value representing one of the discrete actions. :returns: The List containing the branched actions. """ return self.action_lookup[action]
ml-agents/ml-agents-envs/mlagents_envs/envs/unity_gym_env.py/0
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from mlagents_envs.side_channel import SideChannel, IncomingMessage, OutgoingMessage from mlagents_envs.exception import UnityCommunicationException import uuid from enum import IntEnum from typing import List, Tuple class EnvironmentParametersChannel(SideChannel): """ This is the SideChannel for sending environment parameters to Unity. You can send parameters to an environment with the command set_float_parameter. """ class EnvironmentDataTypes(IntEnum): FLOAT = 0 SAMPLER = 1 class SamplerTypes(IntEnum): UNIFORM = 0 GAUSSIAN = 1 MULTIRANGEUNIFORM = 2 def __init__(self) -> None: channel_id = uuid.UUID("534c891e-810f-11ea-a9d0-822485860400") super().__init__(channel_id) def on_message_received(self, msg: IncomingMessage) -> None: raise UnityCommunicationException( "The EnvironmentParametersChannel received a message from Unity, " + "this should not have happened." ) def set_float_parameter(self, key: str, value: float) -> None: """ Sets a float environment parameter in the Unity Environment. :param key: The string identifier of the parameter. :param value: The float value of the parameter. """ msg = OutgoingMessage() msg.write_string(key) msg.write_int32(self.EnvironmentDataTypes.FLOAT) msg.write_float32(value) super().queue_message_to_send(msg) def set_uniform_sampler_parameters( self, key: str, min_value: float, max_value: float, seed: int ) -> None: """ Sets a uniform environment parameter sampler. :param key: The string identifier of the parameter. :param min_value: The minimum of the sampling distribution. :param max_value: The maximum of the sampling distribution. :param seed: The random seed to initialize the sampler. """ msg = OutgoingMessage() msg.write_string(key) msg.write_int32(self.EnvironmentDataTypes.SAMPLER) msg.write_int32(seed) msg.write_int32(self.SamplerTypes.UNIFORM) msg.write_float32(min_value) msg.write_float32(max_value) super().queue_message_to_send(msg) def set_gaussian_sampler_parameters( self, key: str, mean: float, st_dev: float, seed: int ) -> None: """ Sets a gaussian environment parameter sampler. :param key: The string identifier of the parameter. :param mean: The mean of the sampling distribution. :param st_dev: The standard deviation of the sampling distribution. :param seed: The random seed to initialize the sampler. """ msg = OutgoingMessage() msg.write_string(key) msg.write_int32(self.EnvironmentDataTypes.SAMPLER) msg.write_int32(seed) msg.write_int32(self.SamplerTypes.GAUSSIAN) msg.write_float32(mean) msg.write_float32(st_dev) super().queue_message_to_send(msg) def set_multirangeuniform_sampler_parameters( self, key: str, intervals: List[Tuple[float, float]], seed: int ) -> None: """ Sets a multirangeuniform environment parameter sampler. :param key: The string identifier of the parameter. :param intervals: The lists of min and max that define each uniform distribution. :param seed: The random seed to initialize the sampler. """ msg = OutgoingMessage() msg.write_string(key) msg.write_int32(self.EnvironmentDataTypes.SAMPLER) msg.write_int32(seed) msg.write_int32(self.SamplerTypes.MULTIRANGEUNIFORM) flattened_intervals = [value for interval in intervals for value in interval] msg.write_float32_list(flattened_intervals) super().queue_message_to_send(msg)
ml-agents/ml-agents-envs/mlagents_envs/side_channel/environment_parameters_channel.py/0
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from mlagents_envs.envs.unity_aec_env import UnityAECEnv from mlagents_envs.envs.unity_parallel_env import UnityParallelEnv from simple_test_envs import SimpleEnvironment, MultiAgentEnvironment from pettingzoo.test import api_test, parallel_api_test NUM_TEST_CYCLES = 100 def test_single_agent_aec(): unity_env = SimpleEnvironment(["test_single"]) env = UnityAECEnv(unity_env) api_test(env, num_cycles=NUM_TEST_CYCLES, verbose_progress=False) def test_multi_agent_aec(): unity_env = MultiAgentEnvironment(["test_multi_1", "test_multi_2"], num_agents=2) env = UnityAECEnv(unity_env) api_test(env, num_cycles=NUM_TEST_CYCLES, verbose_progress=False) def test_single_agent_parallel(): unity_env = SimpleEnvironment(["test_single"]) env = UnityParallelEnv(unity_env) parallel_api_test(env, num_cycles=NUM_TEST_CYCLES) def test_multi_agent_parallel(): unity_env = MultiAgentEnvironment( ["test_multi_1", "test_multi_2", "test_multi_3"], num_agents=3 ) env = UnityParallelEnv(unity_env) parallel_api_test(env, num_cycles=NUM_TEST_CYCLES)
ml-agents/ml-agents-envs/tests/test_pettingzoo_wrapper.py/0
{ "file_path": "ml-agents/ml-agents-envs/tests/test_pettingzoo_wrapper.py", "repo_id": "ml-agents", "token_count": 435 }
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behaviors: 3DBall: trainer_type: a2c hyperparameters: batch_size: 1000 buffer_size: 1000 learning_rate: 0.0003 beta: 0.001 lambd: 0.99 num_epoch: 1 learning_rate_schedule: linear network_settings: normalize: true hidden_units: 128 num_layers: 2 vis_encode_type: simple reward_signals: extrinsic: gamma: 0.99 strength: 1.0 keep_checkpoints: 5 max_steps: 500000 time_horizon: 1000 summary_freq: 1000
ml-agents/ml-agents-trainer-plugin/mlagents_trainer_plugin/a2c/a2c_3DBall.yaml/0
{ "file_path": "ml-agents/ml-agents-trainer-plugin/mlagents_trainer_plugin/a2c/a2c_3DBall.yaml", "repo_id": "ml-agents", "token_count": 255 }
2,046