//receive trainer Capabilities from android
void ANTPLUG_Receive_trainer_capabilities(string jsonstring)
{
if (request_page_54 == true)
{
request_page_54 = false;
trainerCapabilities = JsonUtility.FromJson <TrainerCapabilities>(jsonstring);
Debug.Log("max resistance is: " + trainerCapabilities.maximumResistance);
Debug.Log("basicResistanceNodeSupport: " + trainerCapabilities.basicResistanceNodeSupport);
Debug.Log("targetPowerModeSupport: " + trainerCapabilities.targetPowerModeSupport);
Debug.Log("simulationModeSupport: " + trainerCapabilities.simulationModeSupport);
}
}
/// <summary>
/// Initializes the environment, configures it and initializes the Academy.
/// </summary>
void InitializeEnvironment()
{
TimerStack.Instance.AddMetadata("communication_protocol_version", k_ApiVersion);
TimerStack.Instance.AddMetadata("com.unity.ml-agents_version", k_PackageVersion);
EnableAutomaticStepping();
SideChannelManager.RegisterSideChannel(new EngineConfigurationChannel());
SideChannelManager.RegisterSideChannel(new TrainingAnalyticsSideChannel());
m_EnvironmentParameters = new EnvironmentParameters();
m_StatsRecorder = new StatsRecorder();
// Try to launch the communicator by using the arguments passed at launch
var port = ReadPortFromArgs();
if (port > 0)
{
Communicator = CommunicatorFactory.Create();
}
if (Communicator != null)
{
// 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.
bool initSuccessful = false;
var communicatorInitParams = new CommunicatorInitParameters
{
port = port,
unityCommunicationVersion = k_ApiVersion,
unityPackageVersion = k_PackageVersion,
name = "AcademySingleton",
CSharpCapabilities = new UnityRLCapabilities()
};
try
{
initSuccessful = Communicator.Initialize(
communicatorInitParams,
out var unityRlInitParameters
);
if (initSuccessful)
{
UnityEngine.Random.InitState(unityRlInitParameters.seed);
// We might have inference-only Agents, so set the seed for them too.
m_InferenceSeed = unityRlInitParameters.seed;
TrainerCapabilities = unityRlInitParameters.TrainerCapabilities;
TrainerCapabilities.WarnOnPythonMissingBaseRLCapabilities();
}
else
{
Debug.Log($"Couldn't connect to trainer on port {port} using API version {k_ApiVersion}. Will perform inference instead.");
Communicator = null;
}
}
catch (Exception ex)
{
Debug.Log($"Unexpected exception when trying to initialize communication: {ex}\nWill 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();
}
