public void TestTimeScaleClamping()
{
OutgoingMessage pythonMsg = new OutgoingMessage();
pythonMsg.WriteInt32((int)EngineConfigurationChannel.ConfigurationType.TimeScale);
pythonMsg.WriteFloat32(1000f);
var sideChannel = new EngineConfigurationChannel();
sideChannel.ProcessMessage(pythonMsg.ToByteArray());
#if UNITY_EDITOR
// Should be clamped
Assert.AreEqual(100.0f, Time.timeScale);
#else
// Not sure we can run this test from a player, but just in case, shouldn't clamp.
Assert.AreEqual(1000.0f, Time.timeScale);
#endif
}
static void Main(string[] args)
{
GradientEngine.UseEnvironmentFromVariable();
TensorFlowSetup.Instance.EnsureInitialized();
Console.Title = "ML Agents";
if (args.Length == 1)
{
SetCpuAffinity(limitCoresTo: int.Parse(args[0]));
}
// debug environment, that does not require Unity: goal is to simply repeat observation
// RunRepeat();
var engineConfigChannel = new EngineConfigurationChannel();
// connect to running Unity, you'll have to press Play there
const string?envName = null;
Console.WriteLine("RELEASE THE KRAKEN!");
var env = new UnityEnvironment(base_port: 5004, file_name: envName,
side_channels: new[] { engineConfigChannel });
try {
engineConfigChannel.set_configuration_parameters(time_scale: 3.3);
env.reset();
// TODO: fix behaviors_specs to be real Dictionary
const string agentGroup = "3DBall?team=0";
BehaviorSpec spec = env.behavior_specs_dyn[agentGroup];
(DecisionSteps, TerminalSteps)stepResult = env.get_steps(agentGroup);
Debug.Assert(stepResult.Item1.obs.Count == 1);
(int agentCount, int observationSize) = ((int, int))((ndarray)stepResult.Item1.obs[0]).shape;
if (!spec.is_action_continuous())
{
throw new NotImplementedException("discrete");
}
var random = new Random();
ndarray RandomActionSampler()
// a list of random values between -1.0 and +1.0
=> (ndarray)ndarray.FromList(Range(0, spec.action_size * agentCount)
.Select(_ => (float)random.NextDouble() * 2 - 1)
.ToList())
.reshape(new int[] { agentCount, spec.action_size })
.astype(PythonClassContainer <float32> .Instance);
SoftActorCritic.SoftActorCritic.Run(new UnityEnvironmentProxy(env),
agentGroup: agentGroup,
actorCriticFactory: ActorCriticFactory,
observationDimensions: observationSize,
actionDimensions: spec.action_size,
actionLimit: 1,
feedFrames: 1,
maxEpisodeLength: 1024,
startSteps: 2048,
replaySize: 1024 * 1024 / 8,
actionSampler: RandomActionSampler);
} finally {
env.close();
}
}
