/// <summary>
/// Sends the academy parameters through the Communicator.
/// Is used by the academy to send the AcademyParameters to the communicator.
/// </summary>
/// <returns>The External Initialization Parameters received.</returns>
/// <param name="academyParameters">The Unity Initialization Parameters to be sent.</param>
public CommunicatorObjects.UnityRLInitializationInput SendAcademyParameters(
CommunicatorObjects.UnityRLInitializationOutput academyParameters)
{
CommunicatorObjects.UnityInput input;
var initializationInput = new CommunicatorObjects.UnityInput();
try
{
initializationInput = m_communicator.Initialize(
new CommunicatorObjects.UnityOutput
{
RlInitializationOutput = academyParameters
},
out input);
}
catch
{
throw new UnityAgentsException(
"The Communicator was unable to connect. Please make sure the External " +
"process is ready to accept communication with Unity.");
}
var firstRlInput = input.RlInput;
m_command = firstRlInput.Command;
m_arenasParameters = input.RlResetInput;
m_isTraining = firstRlInput.IsTraining;
return(initializationInput.RlInitializationInput);
}
/// <summary>
/// Sends the academy parameters through the Communicator.
/// Is used by the academy to send the AcademyParameters to the communicator.
/// </summary>
/// <returns>The External Initialization Parameters received.</returns>
/// <param name="academyParameters">The Unity Initialization Parameters to be sent.</param>
public CommunicatorObjects.UnityRLInitializationInput SendAcademyParameters(
CommunicatorObjects.UnityRLInitializationOutput academyParameters)
{
CommunicatorObjects.UnityInput input;
var initializationInput = new CommunicatorObjects.UnityInput();
try
{
initializationInput = m_Communicator.Initialize(
new CommunicatorObjects.UnityOutput
{
RlInitializationOutput = academyParameters
},
out input);
}
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);
}
var firstRlInput = input.RlInput;
m_Command = firstRlInput.Command;
m_EnvironmentParameters = firstRlInput.EnvironmentParameters;
m_IsTraining = firstRlInput.IsTraining;
return(initializationInput.RlInitializationInput);
}
/// <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()
{
m_OriginalGravity = Physics.gravity;
m_OriginalFixedDeltaTime = Time.fixedDeltaTime;
m_OriginalMaximumDeltaTime = Time.maximumDeltaTime;
InitializeAcademy();
ICommunicator communicator;
var exposedBrains = broadcastHub.broadcastingBrains.Where(x => x != null).ToList();
var controlledBrains = broadcastHub.broadcastingBrains.Where(
x => x != null && x is LearningBrain && broadcastHub.IsControlled(x));
foreach (var brain1 in controlledBrains)
{
var brain = (LearningBrain)brain1;
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
});
}
}
m_BrainBatcher = new Batcher(communicator);
foreach (var trainingBrain in exposedBrains)
{
trainingBrain.SetBatcher(m_BrainBatcher);
}
if (communicator != null)
{
m_IsCommunicatorOn = true;
var academyParameters =
new CommunicatorObjects.UnityRLInitializationOutput();
academyParameters.Name = gameObject.name;
academyParameters.Version = k_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 = m_BrainBatcher.SendAcademyParameters(academyParameters);
Random.InitState(pythonParameters.Seed);
}
// 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.
m_IsInference = !m_IsCommunicatorOn;
BrainDecideAction += () => { };
DestroyAction += () => { };
AgentSetStatus += (i) => { };
AgentResetIfDone += () => { };
AgentSendState += () => { };
AgentAct += () => { };
AgentForceReset += () => { };
// Configure the environment using the configurations provided by
// the developer in the Editor.
SetIsInference(!m_BrainBatcher.GetIsTraining());
ConfigureEnvironment();
}
/// <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);
}
}
