/// This is called by the Academy at the start of the environemnt.
public void InitializeBrain(Academy aca, MLAgents.Batcher brainBatcher)
{
UpdateCoreBrains();
coreBrain.InitializeCoreBrain(brainBatcher);
aca.BrainDecideAction += DecideAction;
isInitialized = true;
}
/// Nothing to implement
/// Nothing to implement
public void InitializeCoreBrain(MLAgents.Batcher brainBatcher)
{
if ((brainBatcher == null) ||
(!broadcast))
{
this.brainBatcher = null;
}
else
{
this.brainBatcher = brainBatcher;
this.brainBatcher.SubscribeBrain(brain.gameObject.name);
}
}
/// Create the reference to decision
public void InitializeCoreBrain(MLAgents.Batcher brainBatcher)
{
decision = brain.gameObject.GetComponent <Decision>();
if ((brainBatcher == null) ||
(!broadcast))
{
this.brainBatcher = null;
}
else
{
this.brainBatcher = brainBatcher;
;
this.brainBatcher.SubscribeBrain(brain.gameObject.name);
}
}
/// Generates the communicator for the Academy if none was present and
/// subscribe to ExternalCommunicator if it was present.
public void InitializeCoreBrain(MLAgents.Batcher brainBatcher)
{
if (brainBatcher == null)
{
brainBatcher = null;
throw new UnityAgentsException(string.Format("The brain {0} was set to" +
" External mode" +
" but Unity was unable to read the" +
" arguments passed at launch.",
brain.gameObject.name));
}
else
{
this.brainBatcher = brainBatcher;
this.brainBatcher.SubscribeBrain(brain.gameObject.name);
}
}
/// <summary>
/// Initializes the environment, configures it and initialized the Academy.
/// </summary>
private void InitializeEnvironment()
{
InitializeAcademy();
Communicator communicator = null;
var exposedBrains = broadcastHub.broadcastingBrains.Where(x => x != null).ToList();;
var controlledBrains = broadcastHub.broadcastingBrains.Where(
x => x != null && x is LearningBrain && broadcastHub.IsControlled(x));
foreach (LearningBrain brain in controlledBrains)
{
brain.SetToControlledExternally();
}
// Try to launch the communicator by usig the arguments passed at launch
try
{
communicator = new RPCCommunicator(
new CommunicatorParameters
{
port = ReadArgs()
});
}
// If it fails, we check if there are any external brains in the scene
// If there are : Launch the communicator on the default port
// If there arn't, there is no need for a communicator and it is set
// to null
catch
{
communicator = null;
if (controlledBrains.ToList().Count > 0)
{
communicator = new RPCCommunicator(
new CommunicatorParameters
{
port = 5005
});
}
}
brainBatcher = new Batcher(communicator);
foreach (var trainingBrain in exposedBrains)
{
trainingBrain.SetBatcher(brainBatcher);
}
if (communicator != null)
{
isCommunicatorOn = true;
var academyParameters =
new CommunicatorObjects.UnityRLInitializationOutput();
academyParameters.Name = gameObject.name;
academyParameters.Version = kApiVersion;
foreach (var brain in exposedBrains)
{
var bp = brain.brainParameters;
academyParameters.BrainParameters.Add(
bp.ToProto(brain.name, broadcastHub.IsControlled(brain)));
}
academyParameters.EnvironmentParameters =
new CommunicatorObjects.EnvironmentParametersProto();
foreach (var key in resetParameters.Keys)
{
academyParameters.EnvironmentParameters.FloatParameters.Add(
key, resetParameters[key]
);
}
var pythonParameters = brainBatcher.SendAcademyParameters(academyParameters);
Random.InitState(pythonParameters.Seed);
Application.logMessageReceived += HandleLog;
logPath = Path.GetFullPath(".") + "/UnitySDK.log";
logWriter = new StreamWriter(logPath, false);
logWriter.WriteLine(System.DateTime.Now.ToString());
logWriter.WriteLine(" ");
logWriter.Close();
}
// If a communicator is enabled/provided, then we assume we are in
// training mode. In the absence of a communicator, we assume we are
// in inference mode.
isInference = !isCommunicatorOn;
BrainDecideAction += () => { };
AgentSetStatus += (m, d, i) => { };
AgentResetIfDone += () => { };
AgentSendState += () => { };
AgentAct += () => { };
AgentForceReset += () => { };
// Configure the environment using the configurations provided by
// the developer in the Editor.
SetIsInference(!brainBatcher.GetIsTraining());
ConfigureEnvironment();
}
/// <summary>
/// Initializes the environment, configures it and initialized the Academy.
/// </summary>
// 初始化
private void InitializeEnvironment()
{
// 保存旧值
originalGravity = Physics.gravity;
originalFixedDeltaTime = Time.fixedDeltaTime;
originalMaximumDeltaTime = Time.maximumDeltaTime;
// 调用虚函数
// InitializeAcademy()
InitializeAcademy();
Communicator communicator = null;
// BroadcastHub: 简单的Brain的列表
// 获取所有的Brain
var exposedBrains = broadcastHub.broadcastingBrains.Where(x => x != null).ToList();;
// 获取受控制的LearningBrain
var controlledBrains = broadcastHub.broadcastingBrains.Where(
x => x != null && x is LearningBrain && broadcastHub.IsControlled(x)
);
// 设置Brain是Controlled
foreach (LearningBrain brain in controlledBrains)
{
// 标记_isControlled = true;
brain.SetToControlledExternally();
}
// Try to launch the communicator by usig the arguments passed at launch
try
{
communicator = new RPCCommunicator(
new CommunicatorParameters
{
// 从命令行，读取要连接的端口号
port = ReadArgs()
});
}
// If it fails, we check if there are any external brains in the scene
// If there are : Launch the communicator on the default port
// If there arn't, there is no need for a communicator and it is set
// to null
catch
{
communicator = null;
// 如果有，需要控制的LearningBrain
// 则表示，需要TensorFlow
// 所以，尝试连接默认端口
if (controlledBrains.ToList().Count > 0)
{
communicator = new RPCCommunicator(
new CommunicatorParameters
{
port = 5005
});
}
}
// 创建Batcher
brainBatcher = new Batcher(communicator);
// Brain设置到Batcher
// Batcher，是用来，连接Brain和Agent的地方
foreach (var trainingBrain in exposedBrains)
{
trainingBrain.SetBatcher(brainBatcher);
}
if (communicator != null)
{
isCommunicatorOn = true;
// 创建UnityRLInitializationOutput消息
var academyParameters = new CommunicatorObjects.UnityRLInitializationOutput();
academyParameters.Name = gameObject.name;
academyParameters.Version = kApiVersion;
// 从需要控制的Brain中
// 获取BrainParameters
// 填写到消息中
foreach (var brain in exposedBrains)
{
var bp = brain.brainParameters;
academyParameters.BrainParameters.Add(bp.ToProto(brain.name, broadcastHub.IsControlled(brain)));
}
// 填写EnvironmentParameters
academyParameters.EnvironmentParameters = new CommunicatorObjects.EnvironmentParametersProto();
foreach (var key in resetParameters.Keys)
{
academyParameters.EnvironmentParameters.FloatParameters.Add(key, resetParameters[key]);
}
// Q: 通过Batcher发送消息？
var pythonParameters = brainBatcher.SendAcademyParameters(academyParameters);
Random.InitState(pythonParameters.Seed);
// 监听Unity消息
Application.logMessageReceived += HandleLog;
// 写入日志
// 当前开始时间
logPath = Path.GetFullPath(".") + "/UnitySDK.log";
using (var fs = File.Open(logPath, FileMode.Append, FileAccess.Write, FileShare.ReadWrite))
{
logWriter = new StreamWriter(fs);
logWriter.WriteLine(System.DateTime.Now.ToString());
logWriter.WriteLine(" ");
logWriter.Close();
}
}
// If a communicator is enabled/provided, then we assume we are in
// training mode. In the absence of a communicator, we assume we are
// in inference mode.
isInference = !isCommunicatorOn;
BrainDecideAction += () => { };
DestroyAction += () => { };
AgentSetStatus += (m, d, i) => { };
AgentResetIfDone += () => { };
AgentSendState += () => { };
AgentAct += () => { };
AgentForceReset += () => { };
// Configure the environment using the configurations provided by
// the developer in the Editor.
SetIsInference(!brainBatcher.GetIsTraining());
ConfigureEnvironment();
}
/// <summary>
/// Initializes the environment, configures it and initialized the Academy.
/// </summary>
private void InitializeEnvironment()
{
// Retrieve Brain and initialize Academy
var brains = GetBrains(gameObject);
InitializeAcademy();
MLAgents.Communicator communicator = null;
// Try to launch the communicator by usig the arguments passed at launch
try
{
communicator = new MLAgents.RPCCommunicator(
new MLAgents.CommunicatorParameters
{
port = ReadArgs()
});
}
// If it fails, we check if there are any external brains in the scene
// If there are : Launch the communicator on the default port
// If there arn't, there is no need for a communicator and it is set
// to null
catch
{
communicator = null;
var externalBrain = brains.FirstOrDefault(b => b.brainType == BrainType.External);
if (externalBrain != null)
{
communicator = new MLAgents.RPCCommunicator(
new MLAgents.CommunicatorParameters
{
port = 5005
});
}
}
brainBatcher = new MLAgents.Batcher(communicator);
// Initialize Brains and communicator (if present)
foreach (var brain in brains)
{
brain.InitializeBrain(this, brainBatcher);
}
if (communicator != null)
{
isCommunicatorOn = true;
var academyParameters =
new MLAgents.CommunicatorObjects.UnityRLInitializationOutput();
academyParameters.Name = gameObject.name;
academyParameters.Version = kApiVersion;
foreach (var brain in brains)
{
var bp = brain.brainParameters;
academyParameters.BrainParameters.Add(
MLAgents.Batcher.BrainParametersConvertor(
bp,
brain.gameObject.name,
(MLAgents.CommunicatorObjects.BrainTypeProto)
brain.brainType));
}
academyParameters.EnvironmentParameters =
new MLAgents.CommunicatorObjects.EnvironmentParametersProto();
foreach (var key in resetParameters.Keys)
{
academyParameters.EnvironmentParameters.FloatParameters.Add(
key, resetParameters[key]
);
}
var pythonParameters = brainBatcher.SendAcademyParameters(academyParameters);
Random.InitState(pythonParameters.Seed);
Application.logMessageReceived += HandleLog;
logPath = Path.GetFullPath(".") + "/unity-environment.log";
logWriter = new StreamWriter(logPath, false);
logWriter.WriteLine(System.DateTime.Now.ToString());
logWriter.WriteLine(" ");
logWriter.Close();
}
// If a communicator is enabled/provided, then we assume we are in
// training mode. In the absence of a communicator, we assume we are
// in inference mode.
isInference = !isCommunicatorOn;
BrainDecideAction += () => { };
AgentSetStatus += (m, d, i) => { };
AgentResetIfDone += () => { };
AgentSendState += () => { };
AgentAct += () => { };
AgentForceReset += () => { };
// Configure the environment using the configurations provided by
// the developer in the Editor.
SetIsInference(!brainBatcher.GetIsTraining());
ConfigureEnvironment();
}
/// Loads the tensorflow graph model to generate a TFGraph object
public void InitializeCoreBrain(MLAgents.Batcher brainBatcher)
{
#if ENABLE_TENSORFLOW
#if UNITY_ANDROID && !UNITY_EDITOR
// This needs to ba called only once and will raise an exception if
// there are multiple internal brains
try{
TensorFlowSharp.Android.NativeBinding.Init();
}
catch {
}
#endif
if ((brainBatcher == null) ||
(!broadcast))
{
this.brainBatcher = null;
}
else
{
this.brainBatcher = brainBatcher;
this.brainBatcher.SubscribeBrain(brain.gameObject.name);
}
if (graphModel != null)
{
graph = new TFGraph();
graph.Import(graphModel.bytes);
session = new TFSession(graph);
// TODO: Make this a loop over a dynamic set of graph inputs
if ((graphScope.Length > 1) && (graphScope[graphScope.Length - 1] != '/'))
{
graphScope = graphScope + '/';
}
if (graph[graphScope + BatchSizePlaceholderName] != null)
{
hasBatchSize = true;
}
if ((graph[graphScope + RecurrentInPlaceholderName] != null) &&
(graph[graphScope + RecurrentOutPlaceholderName] != null))
{
hasRecurrent = true;
var runner = session.GetRunner();
runner.Fetch(graph[graphScope + "memory_size"][0]);
var networkOutput = runner.Run()[0].GetValue();
memorySize = (int)networkOutput;
}
if (graph[graphScope + VectorObservationPlacholderName] != null)
{
hasState = true;
}
if (graph[graphScope + PreviousActionPlaceholderName] != null)
{
hasPrevAction = true;
}
if (graph[graphScope + "value_estimate"] != null)
{
hasValueEstimate = true;
}
if (graph[graphScope + ActionMaskPlaceholderName] != null)
{
hasMaskedActions = true;
}
}
observationMatrixList = new List <float[, , , ]>();
texturesHolder = new List <Texture2D>();
#endif
}
/// <summary>
/// Initializes the environment, configures it and initialized the Academy.
/// </summary>
private void InitializeEnvironment()
{
originalGravity = Physics.gravity;
originalFixedDeltaTime = Time.fixedDeltaTime;
originalMaximumDeltaTime = Time.maximumDeltaTime;
InitializeAcademy();
Communicator communicator = null;
var spawnPrefab = agentSpawner.GetPrefabFor(GetAgentId());
var spawnAgentPrefabs = spawnPrefab.GetComponentsInChildren <Agent>();
var spawnAgentPrefabBrains = spawnAgentPrefabs
.Where(x => x.brain as LearningBrain != null)
.Select(x => x.brain)
.ToList();
var spawnerEnabled = spawnAgentPrefabBrains.Count > 0;
var hubBrains = broadcastHub.broadcastingBrains.Where(x => x != null).ToList();;
var hubControlledBrains = broadcastHub.broadcastingBrains.Where(
x => x != null && x is LearningBrain && broadcastHub.IsControlled(x));
IEnumerable <Brain> exposedBrains =
spawnerEnabled ? spawnAgentPrefabBrains : hubBrains;
IEnumerable <Brain> controlledBrains = hubControlledBrains;
if (spawnerEnabled)
{
controlledBrains = IsTrainingMode()
? spawnAgentPrefabBrains
: new List <Brain>();
}
// Try to launch the communicator by usig the arguments passed at launch
try
{
communicator = new RPCCommunicator(
new CommunicatorParameters
{
port = ReadArgs()
});
}
// If it fails, we check if there are any external brains in the scene
// If there are : Launch the communicator on the default port
// If there arn't, there is no need for a communicator and it is set
// to null
catch
{
communicator = null;
if (controlledBrains.ToList().Count > 0)
{
communicator = new RPCCommunicator(
new CommunicatorParameters
{
port = 5005
});
}
}
foreach (LearningBrain brain in controlledBrains)
{
brain.SetToControlledExternally();
}
brainBatcher = new Batcher(communicator);
foreach (var trainingBrain in exposedBrains)
{
trainingBrain.SetBatcher(brainBatcher);
}
if (communicator != null)
{
isCommunicatorOn = true;
var academyParameters =
new CommunicatorObjects.UnityRLInitializationOutput();
academyParameters.Name = gameObject.name;
academyParameters.Version = kApiVersion;
foreach (var brain in exposedBrains)
{
var bp = brain.brainParameters;
academyParameters.BrainParameters.Add(
bp.ToProto(brain.name, broadcastHub.IsControlled(brain)));
}
academyParameters.EnvironmentParameters =
new CommunicatorObjects.EnvironmentParametersProto();
foreach (var key in resetParameters.Keys)
{
academyParameters.EnvironmentParameters.FloatParameters.Add(
key, resetParameters[key]
);
}
var pythonParameters = brainBatcher.SendAcademyParameters(academyParameters);
Random.InitState(pythonParameters.Seed);
Application.logMessageReceived += HandleLog;
logPath = Path.GetFullPath(".") + "/UnitySDK.log";
using (var fs = File.Open(logPath, FileMode.Append, FileAccess.Write, FileShare.ReadWrite))
{
logWriter = new StreamWriter(fs);
logWriter.WriteLine(System.DateTime.Now.ToString());
logWriter.WriteLine(" ");
logWriter.Close();
}
}
// If a communicator is enabled/provided, then we assume we are in
// training mode. In the absence of a communicator, we assume we are
// in inference mode.
isInference = !isCommunicatorOn;
BrainDecideAction += () => { };
DestroyAction += () => { };
AgentSetStatus += (m, d, i) => { };
AgentResetIfDone += () => { };
AgentSendState += () => { };
AgentAct += () => { };
AgentForceReset += () => { };
// Configure the environment using the configurations provided by
// the developer in the Editor.
SetIsInference(!brainBatcher.GetIsTraining());
ConfigureEnvironment();
if (spawnerEnabled)
{
agentSpawner.SpawnSpawnableEnv(this.gameObject, GetNumAgents(), spawnPrefab);
}
}
