# if UNITY_EDITOR || UNITY_STANDALONE_WIN || UNITY_STANDALONE_OSX || UNITY_STANDALONE_LINUX using Grpc.Core; #endif #if UNITY_EDITOR using UnityEditor; #endif using System; using System.Collections.Generic; using System.Linq; using UnityEngine; using MLAgents.CommunicatorObjects; using MLAgents.Sensors; using MLAgents.Policies; using MLAgents.SideChannels; using Google.Protobuf; namespace MLAgents { /// Responsible for communication with External using gRPC. internal class RpcCommunicator : ICommunicator { public event QuitCommandHandler QuitCommandReceived; public event ResetCommandHandler ResetCommandReceived; /// If true, the communication is active. bool m_IsOpen; List m_BehaviorNames = new List(); bool m_NeedCommunicateThisStep; WriteAdapter m_WriteAdapter = new WriteAdapter(); Dictionary m_SensorShapeValidators = new Dictionary(); Dictionary> m_OrderedAgentsRequestingDecisions = new Dictionary>(); /// The current UnityRLOutput to be sent when all the brains queried the communicator UnityRLOutputProto m_CurrentUnityRlOutput = new UnityRLOutputProto(); Dictionary> m_LastActionsReceived = new Dictionary>(); // Brains that we have sent over the communicator with agents. HashSet m_SentBrainKeys = new HashSet(); Dictionary m_UnsentBrainKeys = new Dictionary(); # if UNITY_EDITOR || UNITY_STANDALONE_WIN || UNITY_STANDALONE_OSX || UNITY_STANDALONE_LINUX /// The Unity to External client. UnityToExternalProto.UnityToExternalProtoClient m_Client; #endif /// The communicator parameters sent at construction CommunicatorInitParameters m_CommunicatorInitParameters; /// /// Initializes a new instance of the RPCCommunicator class. /// /// Communicator parameters. public RpcCommunicator(CommunicatorInitParameters communicatorInitParameters) { m_CommunicatorInitParameters = communicatorInitParameters; } #region Initialization internal static bool CheckCommunicationVersionsAreCompatible( string unityCommunicationVersion, string pythonApiVersion, string pythonLibraryVersion) { var unityVersion = new Version(unityCommunicationVersion); var pythonVersion = new Version(pythonApiVersion); if (unityVersion.Major == 0) { if (unityVersion.Major != pythonVersion.Major || unityVersion.Minor != pythonVersion.Minor) { return false; } } else if (unityVersion.Major != pythonVersion.Major) { return false; } else if (unityVersion.Minor != pythonVersion.Minor) { // Even if we initialize, we still want to check to make sure that we inform users of minor version // changes. This will surface any features that may not work due to minor version incompatibilities. Debug.LogWarningFormat( "WARNING: The communication API versions between Unity and python differ at the minor version level. " + "Python API: {0}, Unity API: {1} Python Library Version: {2} .\n" + "This means that some features may not work unless you upgrade the package with the lower version." + "Please find the versions that work best together from our release page.\n" + "https://github.com/Unity-Technologies/ml-agents/releases", pythonApiVersion, unityCommunicationVersion, pythonLibraryVersion ); } return true; } /// /// Sends the initialization parameters through the Communicator. /// Is used by the academy to send initialization parameters to the communicator. /// /// The External Initialization Parameters received. /// The Unity Initialization Parameters to be sent. public UnityRLInitParameters Initialize(CommunicatorInitParameters initParameters) { var academyParameters = new UnityRLInitializationOutputProto { Name = initParameters.name, PackageVersion = initParameters.unityPackageVersion, CommunicationVersion = initParameters.unityCommunicationVersion }; UnityInputProto input; UnityInputProto initializationInput; try { initializationInput = Initialize( new UnityOutputProto { RlInitializationOutput = academyParameters }, out input); var pythonCommunicationVersion = initializationInput.RlInitializationInput.CommunicationVersion; var pythonPackageVersion = initializationInput.RlInitializationInput.PackageVersion; var unityCommunicationVersion = initParameters.unityCommunicationVersion; var communicationIsCompatible = CheckCommunicationVersionsAreCompatible(unityCommunicationVersion, pythonCommunicationVersion, pythonPackageVersion); // Initialization succeeded part-way. The most likely cause is a mismatch between the communicator // API strings, so log an explicit warning if that's the case. if (initializationInput != null && input == null) { if (!communicationIsCompatible) { Debug.LogWarningFormat( "Communication protocol between python ({0}) and Unity ({1}) have different " + "versions which make them incompatible. Python library version: {2}.", pythonCommunicationVersion, initParameters.unityCommunicationVersion, pythonPackageVersion ); } else { Debug.LogWarningFormat( "Unknown communication error between Python. Python communication protocol: {0}, " + "Python library version: {1}.", pythonCommunicationVersion, pythonPackageVersion ); } throw new UnityAgentsException("ICommunicator.Initialize() failed."); } } catch { var exceptionMessage = "The Communicator was unable to connect. Please make sure the External " + "process is ready to accept communication with Unity."; // Check for common error condition and add details to the exception message. var httpProxy = Environment.GetEnvironmentVariable("HTTP_PROXY"); var httpsProxy = Environment.GetEnvironmentVariable("HTTPS_PROXY"); if (httpProxy != null || httpsProxy != null) { exceptionMessage += " Try removing HTTP_PROXY and HTTPS_PROXY from the" + "environment variables and try again."; } throw new UnityAgentsException(exceptionMessage); } UpdateEnvironmentWithInput(input.RlInput); return initializationInput.RlInitializationInput.ToUnityRLInitParameters(); } /// /// Adds the brain to the list of brains which will be sending information to External. /// /// Brain key. /// Brain parameters needed to send to the trainer. public void SubscribeBrain(string brainKey, BrainParameters brainParameters) { if (m_BehaviorNames.Contains(brainKey)) { return; } m_BehaviorNames.Add(brainKey); m_CurrentUnityRlOutput.AgentInfos.Add( brainKey, new UnityRLOutputProto.Types.ListAgentInfoProto() ); CacheBrainParameters(brainKey, brainParameters); } void UpdateEnvironmentWithInput(UnityRLInputProto rlInput) { SideChannelUtils.ProcessSideChannelData(rlInput.SideChannel.ToArray()); SendCommandEvent(rlInput.Command); } UnityInputProto Initialize(UnityOutputProto unityOutput, out UnityInputProto unityInput) { # if UNITY_EDITOR || UNITY_STANDALONE_WIN || UNITY_STANDALONE_OSX || UNITY_STANDALONE_LINUX m_IsOpen = true; var channel = new Channel( "localhost:" + m_CommunicatorInitParameters.port, ChannelCredentials.Insecure); m_Client = new UnityToExternalProto.UnityToExternalProtoClient(channel); var result = m_Client.Exchange(WrapMessage(unityOutput, 200)); unityInput = m_Client.Exchange(WrapMessage(null, 200)).UnityInput; #if UNITY_EDITOR EditorApplication.playModeStateChanged += HandleOnPlayModeChanged; #endif return result.UnityInput; #else throw new UnityAgentsException( "You cannot perform training on this platform."); #endif } #endregion #region Destruction /// /// Close the communicator gracefully on both sides of the communication. /// public void Dispose() { # if UNITY_EDITOR || UNITY_STANDALONE_WIN || UNITY_STANDALONE_OSX || UNITY_STANDALONE_LINUX if (!m_IsOpen) { return; } try { m_Client.Exchange(WrapMessage(null, 400)); m_IsOpen = false; } catch { // ignored } #else throw new UnityAgentsException( "You cannot perform training on this platform."); #endif } #endregion #region Sending Events void SendCommandEvent(CommandProto command) { switch (command) { case CommandProto.Quit: { QuitCommandReceived?.Invoke(); return; } case CommandProto.Reset: { foreach (var brainName in m_OrderedAgentsRequestingDecisions.Keys) { m_OrderedAgentsRequestingDecisions[brainName].Clear(); } ResetCommandReceived?.Invoke(); return; } default: { return; } } } #endregion #region Sending and retreiving data public void DecideBatch() { if (!m_NeedCommunicateThisStep) { return; } m_NeedCommunicateThisStep = false; SendBatchedMessageHelper(); } /// /// Sends the observations of one Agent. /// /// Batch Key. /// Agent info. /// Sensors that will produce the observations public void PutObservations(string behaviorName, AgentInfo info, List sensors) { # if DEBUG if (!m_SensorShapeValidators.ContainsKey(behaviorName)) { m_SensorShapeValidators[behaviorName] = new SensorShapeValidator(); } m_SensorShapeValidators[behaviorName].ValidateSensors(sensors); #endif using (TimerStack.Instance.Scoped("AgentInfo.ToProto")) { var agentInfoProto = info.ToAgentInfoProto(); using (TimerStack.Instance.Scoped("GenerateSensorData")) { foreach (var sensor in sensors) { var obsProto = sensor.GetObservationProto(m_WriteAdapter); agentInfoProto.Observations.Add(obsProto); } } m_CurrentUnityRlOutput.AgentInfos[behaviorName].Value.Add(agentInfoProto); } m_NeedCommunicateThisStep = true; if (!m_OrderedAgentsRequestingDecisions.ContainsKey(behaviorName)) { m_OrderedAgentsRequestingDecisions[behaviorName] = new List(); } if (!info.done) { m_OrderedAgentsRequestingDecisions[behaviorName].Add(info.episodeId); } if (!m_LastActionsReceived.ContainsKey(behaviorName)) { m_LastActionsReceived[behaviorName] = new Dictionary(); } m_LastActionsReceived[behaviorName][info.episodeId] = null; if (info.done) { m_LastActionsReceived[behaviorName].Remove(info.episodeId); } } /// /// Helper method that sends the current UnityRLOutput, receives the next UnityInput and /// Applies the appropriate AgentAction to the agents. /// void SendBatchedMessageHelper() { var message = new UnityOutputProto { RlOutput = m_CurrentUnityRlOutput, }; var tempUnityRlInitializationOutput = GetTempUnityRlInitializationOutput(); if (tempUnityRlInitializationOutput != null) { message.RlInitializationOutput = tempUnityRlInitializationOutput; } byte[] messageAggregated = SideChannelUtils.GetSideChannelMessage(); message.RlOutput.SideChannel = ByteString.CopyFrom(messageAggregated); var input = Exchange(message); UpdateSentBrainParameters(tempUnityRlInitializationOutput); foreach (var k in m_CurrentUnityRlOutput.AgentInfos.Keys) { m_CurrentUnityRlOutput.AgentInfos[k].Value.Clear(); } var rlInput = input?.RlInput; if (rlInput?.AgentActions == null) { return; } UpdateEnvironmentWithInput(rlInput); foreach (var brainName in rlInput.AgentActions.Keys) { if (!m_OrderedAgentsRequestingDecisions[brainName].Any()) { continue; } if (!rlInput.AgentActions[brainName].Value.Any()) { continue; } var agentActions = rlInput.AgentActions[brainName].ToAgentActionList(); var numAgents = m_OrderedAgentsRequestingDecisions[brainName].Count; for (var i = 0; i < numAgents; i++) { var agentAction = agentActions[i]; var agentId = m_OrderedAgentsRequestingDecisions[brainName][i]; if (m_LastActionsReceived[brainName].ContainsKey(agentId)) { m_LastActionsReceived[brainName][agentId] = agentAction.vectorActions; } } } foreach (var brainName in m_OrderedAgentsRequestingDecisions.Keys) { m_OrderedAgentsRequestingDecisions[brainName].Clear(); } } public float[] GetActions(string behaviorName, int agentId) { if (m_LastActionsReceived.ContainsKey(behaviorName)) { if (m_LastActionsReceived[behaviorName].ContainsKey(agentId)) { return m_LastActionsReceived[behaviorName][agentId]; } } return null; } /// /// Send a UnityOutput and receives a UnityInput. /// /// The next UnityInput. /// The UnityOutput to be sent. UnityInputProto Exchange(UnityOutputProto unityOutput) { # if UNITY_EDITOR || UNITY_STANDALONE_WIN || UNITY_STANDALONE_OSX || UNITY_STANDALONE_LINUX if (!m_IsOpen) { return null; } try { var message = m_Client.Exchange(WrapMessage(unityOutput, 200)); if (message.Header.Status == 200) { return message.UnityInput; } m_IsOpen = false; // Not sure if the quit command is actually sent when a // non 200 message is received. Notify that we are indeed // quitting. QuitCommandReceived?.Invoke(); return message.UnityInput; } catch { m_IsOpen = false; QuitCommandReceived?.Invoke(); return null; } #else throw new UnityAgentsException( "You cannot perform training on this platform."); #endif } /// /// Wraps the UnityOutput into a message with the appropriate status. /// /// The UnityMessage corresponding. /// The UnityOutput to be wrapped. /// The status of the message. static UnityMessageProto WrapMessage(UnityOutputProto content, int status) { return new UnityMessageProto { Header = new HeaderProto { Status = status }, UnityOutput = content }; } void CacheBrainParameters(string behaviorName, BrainParameters brainParameters) { if (m_SentBrainKeys.Contains(behaviorName)) { return; } // TODO We should check that if m_unsentBrainKeys has brainKey, it equals brainParameters m_UnsentBrainKeys[behaviorName] = brainParameters; } UnityRLInitializationOutputProto GetTempUnityRlInitializationOutput() { UnityRLInitializationOutputProto output = null; foreach (var behaviorName in m_UnsentBrainKeys.Keys) { if (m_CurrentUnityRlOutput.AgentInfos.ContainsKey(behaviorName)) { if (m_CurrentUnityRlOutput.AgentInfos[behaviorName].CalculateSize() > 0) { // Only send the BrainParameters if there is a non empty list of // AgentInfos ready to be sent. // This is to ensure that The Python side will always have a first // observation when receiving the BrainParameters if (output == null) { output = new UnityRLInitializationOutputProto(); } var brainParameters = m_UnsentBrainKeys[behaviorName]; output.BrainParameters.Add(brainParameters.ToProto(behaviorName, true)); } } } return output; } void UpdateSentBrainParameters(UnityRLInitializationOutputProto output) { if (output == null) { return; } foreach (var brainProto in output.BrainParameters) { m_SentBrainKeys.Add(brainProto.BrainName); m_UnsentBrainKeys.Remove(brainProto.BrainName); } } #endregion #if UNITY_EDITOR /// /// When the editor exits, the communicator must be closed /// /// State. void HandleOnPlayModeChanged(PlayModeStateChange state) { // This method is run whenever the playmode state is changed. if (state == PlayModeStateChange.ExitingPlayMode) { Dispose(); } } #endif } }