/// <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 Paramters to be sent.</param>
public UnityRLInitializationInput SendAcademyParameters(
UnityRLInitializationOutput academyParameters)
{
UnityInput input;
var initializationInput = new UnityInput();
try
{
initializationInput = m_communicator.Initialize(
new 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_environmentParameters = firstRlInput.EnvironmentParameters;
m_isTraining = firstRlInput.IsTraining;
return(initializationInput.RlInitializationInput);
}
/// <summary>
/// Initializes the environment, configures it and initialized the Academy.
/// </summary>
private void InitializeEnvironment()
{
// Retrieve Brain and initialize Academy
var brains = GetBrains(gameObject);
InitializeAcademy();
Communicator communicator = null;
// 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;
var externalBrain = brains.FirstOrDefault(b => b.brainType == BrainType.External);
if (externalBrain != null)
{
communicator = new RPCCommunicator(
new CommunicatorParameters
{
port = 5005
});
}
}
brainBatcher = new 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 UnityRLInitializationOutput();
academyParameters.Name = gameObject.name;
academyParameters.Version = kApiVersion;
foreach (var brain in brains)
{
var bp = brain.brainParameters;
academyParameters.BrainParameters.Add(
Batcher.BrainParametersConvertor(
bp,
brain.gameObject.name,
(BrainTypeProto)
brain.brainType));
}
academyParameters.EnvironmentParameters =
new 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(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();
}