/// <summary>
/// Initializes the environment, configures it and initialized the Academy.
/// </summary>
void InitializeEnvironment()
{
EnableAutomaticStepping();
var floatProperties = new FloatPropertiesChannel();
FloatProperties = floatProperties;
// Try to launch the communicator by using the arguments passed at launch
var port = ReadPortFromArgs();
if (port > 0)
{
Communicator = new RpcCommunicator(
new CommunicatorInitParameters
{
port = port
}
);
}
if (Communicator != null)
{
Communicator.RegisterSideChannel(new EngineConfigurationChannel());
Communicator.RegisterSideChannel(floatProperties);
// We try to exchange the first message with Python. If this fails, it means
// no Python Process is ready to train the environment. In this case, the
//environment must use Inference.
try
{
var unityRlInitParameters = Communicator.Initialize(
new CommunicatorInitParameters
{
version = k_ApiVersion,
name = "AcademySingleton",
});
UnityEngine.Random.InitState(unityRlInitParameters.seed);
}
catch
{
Debug.Log($"" +
$"Couldn't connect to trainer on port {port} using API version {k_ApiVersion}. " +
"Will perform inference instead."
);
Communicator = null;
}
if (Communicator != null)
{
Communicator.QuitCommandReceived += OnQuitCommandReceived;
Communicator.ResetCommandReceived += OnResetCommand;
}
}
// 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.
ResetActions();
}
/// <summary>
/// Shut down the Academy.
/// </summary>
public void Dispose()
{
DisableAutomaticStepping();
// Signal to listeners that the academy is being destroyed now
DestroyAction?.Invoke();
Communicator?.Dispose();
Communicator = null;
if (m_ModelRunners != null)
{
foreach (var mr in m_ModelRunners)
{
mr.Dispose();
}
m_ModelRunners = null;
}
// Clear out the actions so we're not keeping references to any old objects
ResetActions();
// TODO - Pass worker ID or some other identifier,
// so that multiple envs won't overwrite each others stats.
TimerStack.Instance.SaveJsonTimers();
FloatProperties = null;
m_Initialized = false;
// Reset the Lazy instance
s_Lazy = new Lazy <Academy>(() => new Academy());
}
/// <summary>
/// Initializes the environment, configures it and initialized the Academy.
/// </summary>
void InitializeEnvironment()
{
m_OriginalGravity = Physics.gravity;
m_OriginalFixedDeltaTime = Time.fixedDeltaTime;
m_OriginalMaximumDeltaTime = Time.maximumDeltaTime;
var floatProperties = new FloatPropertiesChannel();
FloatProperties = floatProperties;
InitializeAcademy();
// Try to launch the communicator by using the arguments passed at launch
var port = ReadPortFromArgs();
if (port > 0)
{
Communicator = new RpcCommunicator(
new CommunicatorInitParameters
{
port = port
}
);
}
if (Communicator != null)
{
Communicator.RegisterSideChannel(new EngineConfigurationChannel());
Communicator.RegisterSideChannel(floatProperties);
// We try to exchange the first message with Python. If this fails, it means
// no Python Process is ready to train the environment. In this case, the
//environment must use Inference.
try
{
var unityRLInitParameters = Communicator.Initialize(
new CommunicatorInitParameters
{
version = k_ApiVersion,
name = gameObject.name,
});
Random.InitState(unityRLInitParameters.seed);
}
catch
{
Debug.Log($"" +
$"Couldn't connect to trainer on port {port} using API version {k_ApiVersion}. " +
"Will perform inference instead."
);
Communicator = null;
}
if (Communicator != null)
{
Communicator.QuitCommandReceived += OnQuitCommandReceived;
Communicator.ResetCommandReceived += OnResetCommand;
}
}
// 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.
DecideAction += () => { };
DestroyAction += () => { };
AgentSetStatus += i => { };
AgentResetIfDone += () => { };
AgentSendState += () => { };
AgentAct += () => { };
AgentForceReset += () => { };
}