public override void ResetTrainer()
{
base.ResetTrainer();
var agents = statesEpisodeHistory.Keys;
stats.ClearAll();
statesEpisodeHistory.Clear();
rewardsEpisodeHistory.Clear();
actionprobsEpisodeHistory.Clear();
actionsEpisodeHistory.Clear();
valuesEpisodeHistory.Clear();
accumulatedRewards.Clear();
episodeSteps.Clear();
dataBuffer.ClearData();
foreach (var agent in agents)
{
if (agent)
{
agent.AgentReset();
}
}
goodEpisodeHistory.Clear();
policyTrainEpisodeHistory.Clear();
if (exampleEpisodes != null)
{
goodEpisodeHistory = LoadHistory(exampleEpisodes.bytes);
}
}
public void SaveHistory(SortedRawHistory history, string savefilename)
{
if (string.IsNullOrEmpty(savefilename))
{
Debug.Log("savefilename empty. history not saved.");
return;
}
//serailzie the data and save it to path
var binFormatter = new BinaryFormatter();
var mStream = new MemoryStream();
binFormatter.Serialize(mStream, history);
byte[] data = mStream.ToArray();
var fullPath = Path.GetFullPath(Path.Combine(checkpointPath, savefilename));
fullPath = fullPath.Replace('/', Path.DirectorySeparatorChar);
fullPath = fullPath.Replace('\\', Path.DirectorySeparatorChar);
Directory.CreateDirectory(Path.GetDirectoryName(fullPath));
File.WriteAllBytes(fullPath, data);
Debug.Log("history saved to " + fullPath);
}
public override void Initialize()
{
iModelHPPO = modelRef as IRLModelHPPO;
Debug.Assert(iModelHPPO != null, "Please assign a model that implement interface IRLModelHPPO to modelRef");
parametersPPO = parameters as TrainerParamsPPO;
Debug.Assert(parametersPPO != null, "Please Specify PPO Trainer Parameters");
Debug.Assert(BrainToTrain != null, "brain can not be null");
//initialize all data buffers
statesEpisodeHistory = new Dictionary <Agent, List <float> >();
rewardsEpisodeHistory = new Dictionary <Agent, List <float> >();
actionsEpisodeHistory = new Dictionary <Agent, List <float> >();
actionprobsEpisodeHistory = new Dictionary <Agent, List <float> >();
valuesEpisodeHistory = new Dictionary <Agent, List <float> >();
visualEpisodeHistory = new Dictionary <Agent, List <List <float[, , ]> > >();
actionMasksEpisodeHistory = new Dictionary <Agent, List <List <float> > >();
accumulatedRewards = new Dictionary <Agent, float>();
episodeSteps = new Dictionary <Agent, int>();
var brainParameters = BrainToTrain.brainParameters;
Debug.Assert(brainParameters.vectorActionSize.Length > 0, "Action size can not be zero. Please set it in the brain");
List <DataBuffer.DataInfo> allBufferData = new List <DataBuffer.DataInfo>()
{
new DataBuffer.DataInfo("Action", typeof(float), new int[] { brainParameters.vectorActionSpaceType == SpaceType.continuous ? brainParameters.vectorActionSize[0] : brainParameters.vectorActionSize.Length }),
new DataBuffer.DataInfo("ActionProb", typeof(float), new int[] { brainParameters.vectorActionSpaceType == SpaceType.continuous ? brainParameters.vectorActionSize[0] : brainParameters.vectorActionSize.Length }),
new DataBuffer.DataInfo("TargetValue", typeof(float), new int[] { 1 }),
new DataBuffer.DataInfo("OldValue", typeof(float), new int[] { 1 }),
new DataBuffer.DataInfo("Advantage", typeof(float), new int[] { 1 })
};
if (brainParameters.vectorObservationSize > 0)
{
allBufferData.Add(new DataBuffer.DataInfo("VectorObservation", typeof(float), new int[] { brainParameters.vectorObservationSize *brainParameters.numStackedVectorObservations }));
}
for (int i = 0; i < brainParameters.cameraResolutions.Length; ++i)
{
int width = brainParameters.cameraResolutions[i].width;
int height = brainParameters.cameraResolutions[i].height;
int channels;
if (brainParameters.cameraResolutions[i].blackAndWhite)
{
channels = 1;
}
else
{
channels = 3;
}
allBufferData.Add(new DataBuffer.DataInfo("VisualObservation" + i, typeof(float), new int[] { height, width, channels }));
}
if (brainParameters.vectorActionSpaceType == SpaceType.discrete)
{
for (int i = 0; i < brainParameters.vectorActionSize.Length; ++i)
{
allBufferData.Add(new DataBuffer.DataInfo("ActionMask" + i, typeof(float), new int[] { brainParameters.vectorActionSize[i] }));
}
}
dataBuffer = new DataBuffer(allBufferData.ToArray());
policyTrainBuffer = new DataBuffer(allBufferData.ToArray());
tempGoodTrainBuffer = new DataBuffer(allBufferData.ToArray());
policyTrainEpisodeHistory = new SortedRawHistory();
goodEpisodeHistory = new SortedRawHistory(goodHistoryCount);
//exampleHistory = null;
if (exampleEpisodes != null)
{
goodEpisodeHistory = LoadHistory(exampleEpisodes.bytes);
}
//initialize loggers and neuralnetowrk model
stats = new StatsLogger();
modelRef.Initialize(BrainToTrain.brainParameters, isTraining, parameters);
if (continueFromCheckpoint)
{
LoadModel();
}
}
