public Agent(IxTrainerCallback icallback, MyCaffeControl <T> mycaffe, PropertySet properties, CryptoRandom random, Phase phase)
{
m_icallback = icallback;
m_brain = new Brain <T>(mycaffe, properties, random, phase);
m_properties = properties;
m_random = random;
m_fGamma = (float)properties.GetPropertyAsDouble("Gamma", 0.99);
m_bAllowDiscountReset = properties.GetPropertyAsBool("AllowDiscountReset", false);
m_bUseRawInput = properties.GetPropertyAsBool("UseRawInput", false);
}
/// <summary>
/// The constructor.
/// </summary>
/// <param name="mycaffe">Specifies the instance of MyCaffe assoiated with the open project - when using more than one Brain, this is the master project.</param>
/// <param name="properties">Specifies the properties passed into the trainer.</param>
/// <param name="random">Specifies the random number generator used.</param>
/// <param name="phase">Specifies the phase under which to run.</param>
public Brain(MyCaffeControl <T> mycaffe, PropertySet properties, CryptoRandom random, Phase phase)
{
m_mycaffe = mycaffe;
m_solver = mycaffe.GetInternalSolver();
m_netOutput = mycaffe.GetInternalNet(phase);
m_netTarget = new Net <T>(m_mycaffe.Cuda, m_mycaffe.Log, m_netOutput.net_param, m_mycaffe.CancelEvent, null, phase);
m_properties = properties;
m_random = random;
Blob <T> data = m_netOutput.blob_by_name("data");
if (data == null)
{
m_mycaffe.Log.FAIL("Missing the expected input 'data' blob!");
}
m_nBatchSize = data.num;
Blob <T> logits = m_netOutput.blob_by_name("logits");
if (logits == null)
{
m_mycaffe.Log.FAIL("Missing the expected input 'logits' blob!");
}
m_nActionCount = logits.channels;
m_transformer = m_mycaffe.DataTransformer;
m_blobActions = new Blob <T>(m_mycaffe.Cuda, m_mycaffe.Log, false);
m_blobQValue = new Blob <T>(m_mycaffe.Cuda, m_mycaffe.Log);
m_blobNextQValue = new Blob <T>(m_mycaffe.Cuda, m_mycaffe.Log);
m_blobExpectedQValue = new Blob <T>(m_mycaffe.Cuda, m_mycaffe.Log);
m_blobDone = new Blob <T>(m_mycaffe.Cuda, m_mycaffe.Log, false);
m_blobLoss = new Blob <T>(m_mycaffe.Cuda, m_mycaffe.Log);
m_blobWeights = new Blob <T>(m_mycaffe.Cuda, m_mycaffe.Log, false);
m_fGamma = (float)properties.GetPropertyAsDouble("Gamma", m_fGamma);
m_memLoss = m_netOutput.FindLastLayer(LayerParameter.LayerType.MEMORY_LOSS) as MemoryLossLayer <T>;
if (m_memLoss == null)
{
m_mycaffe.Log.FAIL("Missing the expected MEMORY_LOSS layer!");
}
double?dfRate = mycaffe.CurrentProject.GetSolverSettingAsNumeric("base_lr");
if (dfRate.HasValue)
{
m_dfLearningRate = dfRate.Value;
}
m_nMiniBatch = m_properties.GetPropertyAsInt("MiniBatch", m_nMiniBatch);
m_bUseAcceleratedTraining = properties.GetPropertyAsBool("UseAcceleratedTraining", false);
if (m_nMiniBatch > 1)
{
m_colAccumulatedGradients = m_netOutput.learnable_parameters.Clone();
m_colAccumulatedGradients.SetDiff(0);
}
}
/// <summary>
/// The constructor.
/// </summary>
/// <param name="icallback">Specifies the callback used for update notifications sent to the parent.</param>
/// <param name="mycaffe">Specifies the instance of MyCaffe with the open project.</param>
/// <param name="properties">Specifies the properties passed into the trainer.</param>
/// <param name="random">Specifies the random number generator used.</param>
/// <param name="phase">Specifies the phase of the internal network to use.</param>
public DqnAgent(IxTrainerCallback icallback, MyCaffeControl <T> mycaffe, PropertySet properties, CryptoRandom random, Phase phase)
{
m_icallback = icallback;
m_brain = new Brain <T>(mycaffe, properties, random, phase);
m_properties = properties;
m_random = random;
m_fGamma = (float)properties.GetPropertyAsDouble("Gamma", m_fGamma);
m_bUseRawInput = properties.GetPropertyAsBool("UseRawInput", m_bUseRawInput);
m_nMaxMemory = properties.GetPropertyAsInt("MaxMemory", m_nMaxMemory);
m_nTrainingUpdateFreq = properties.GetPropertyAsInt("TrainingUpdateFreq", m_nTrainingUpdateFreq);
m_nExplorationNum = properties.GetPropertyAsInt("ExplorationNum", m_nExplorationNum);
m_nEpsSteps = properties.GetPropertyAsInt("EpsSteps", m_nEpsSteps);
m_dfEpsStart = properties.GetPropertyAsDouble("EpsStart", m_dfEpsStart);
m_dfEpsEnd = properties.GetPropertyAsDouble("EpsEnd", m_dfEpsEnd);
m_dfEpsDelta = (m_dfEpsStart - m_dfEpsEnd) / m_nEpsSteps;
m_dfExplorationRate = m_dfEpsStart;
if (m_dfEpsStart < 0 || m_dfEpsStart > 1)
{
throw new Exception("The 'EpsStart' is out of range - please specify a real number in the range [0,1]");
}
if (m_dfEpsEnd < 0 || m_dfEpsEnd > 1)
{
throw new Exception("The 'EpsEnd' is out of range - please specify a real number in the range [0,1]");
}
if (m_dfEpsEnd > m_dfEpsStart)
{
throw new Exception("The 'EpsEnd' must be less than the 'EpsStart' value.");
}
}
/// <summary>
/// Initialize the gym with the specified properties.
/// </summary>
/// <param name="log">Specifies the output log to use.</param>
/// <param name="properties">Specifies the properties containing Gym specific initialization parameters.</param>
/// <remarks>
/// The ModelGym uses the following initialization properties.
///
/// 'GpuID' - the GPU to run on.
/// 'ModelDescription' - the model description of the model to use.
/// 'Dataset' - the name of the dataset to use.
/// 'Weights' - the model trained weights.
/// 'CudaPath' - the path of the CudaDnnDLL to use.
/// 'BatchSize' - the batch size used when running images through the model (default = 16).
/// 'RecreateData' - when 'True' the data is re-run through the model, otherwise if already run the data is loaded from file (faster).
/// </remarks>
public void Initialize(Log log, PropertySet properties)
{
m_nGpuID = properties.GetPropertyAsInt("GpuID");
m_strModelDesc = properties.GetProperty("ModelDescription");
m_strDataset = properties.GetProperty("Dataset");
m_rgWeights = properties.GetPropertyBlob("Weights");
m_nBatchSize = properties.GetPropertyAsInt("BatchSize", 16);
m_bRecreateData = properties.GetPropertyAsBool("RecreateData", false);
m_strProject = properties.GetProperty("ProjectName");
if (string.IsNullOrEmpty(m_strProject))
{
m_strProject = "default";
}
string strCudaPath = properties.GetProperty("CudaPath");
SettingsCaffe s = new SettingsCaffe();
s.GpuIds = m_nGpuID.ToString();
s.ImageDbLoadMethod = IMAGEDB_LOAD_METHOD.LOAD_ON_DEMAND_BACKGROUND;
m_imgdb = new MyCaffeImageDatabase2(log);
m_imgdb.InitializeWithDsName1(s, m_strDataset);
m_ds = m_imgdb.GetDatasetByName(m_strDataset);
SimpleDatum sd = m_imgdb.QueryImage(m_ds.TrainingSource.ID, 0, IMGDB_LABEL_SELECTION_METHOD.NONE, IMGDB_IMAGE_SELECTION_METHOD.NONE);
BlobShape shape = new BlobShape(1, sd.Channels, sd.Height, sd.Width);
if (m_evtCancel == null)
{
m_evtCancel = new CancelEvent();
}
m_mycaffe = new MyCaffeControl <float>(s, log, m_evtCancel, null, null, null, null, strCudaPath);
m_mycaffe.LoadToRun(m_strModelDesc, m_rgWeights, shape);
m_log = log;
}
public Brain(MyCaffeControl <T> mycaffe, PropertySet properties, CryptoRandom random, IxTrainerCallbackRNN icallback, Phase phase, BucketCollection rgVocabulary, bool bUsePreloadData, string strRunProperties = null)
{
string strOutputBlob = null;
if (strRunProperties != null)
{
m_runProperties = new PropertySet(strRunProperties);
}
m_icallback = icallback;
m_mycaffe = mycaffe;
m_properties = properties;
m_random = random;
m_rgVocabulary = rgVocabulary;
m_bUsePreloadData = bUsePreloadData;
m_nSolverSequenceLength = m_properties.GetPropertyAsInt("SequenceLength", -1);
m_bDisableVocabulary = m_properties.GetPropertyAsBool("DisableVocabulary", false);
m_nThreads = m_properties.GetPropertyAsInt("Threads", 1);
m_dfScale = m_properties.GetPropertyAsDouble("Scale", 1.0);
if (m_nThreads > 1)
{
m_dataPool.Initialize(m_nThreads, icallback);
}
if (m_runProperties != null)
{
m_dfTemperature = Math.Abs(m_runProperties.GetPropertyAsDouble("Temperature", 0));
if (m_dfTemperature > 1.0)
{
m_dfTemperature = 1.0;
}
string strPhaseOnRun = m_runProperties.GetProperty("PhaseOnRun", false);
switch (strPhaseOnRun)
{
case "RUN":
m_phaseOnRun = Phase.RUN;
break;
case "TEST":
m_phaseOnRun = Phase.TEST;
break;
case "TRAIN":
m_phaseOnRun = Phase.TRAIN;
break;
}
if (phase == Phase.RUN && m_phaseOnRun != Phase.NONE)
{
if (m_phaseOnRun != Phase.RUN)
{
m_mycaffe.Log.WriteLine("Warning: Running on the '" + m_phaseOnRun.ToString() + "' network.");
}
strOutputBlob = m_runProperties.GetProperty("OutputBlob", false);
if (strOutputBlob == null)
{
throw new Exception("You must specify the 'OutputBlob' when Running with a phase other than RUN.");
}
strOutputBlob = Utility.Replace(strOutputBlob, '~', ';');
phase = m_phaseOnRun;
}
}
m_net = mycaffe.GetInternalNet(phase);
if (m_net == null)
{
mycaffe.Log.WriteLine("WARNING: Test net does not exist, set test_iteration > 0. Using TRAIN phase instead.");
m_net = mycaffe.GetInternalNet(Phase.TRAIN);
}
// Find the first LSTM layer to determine how to load the data.
// NOTE: Only LSTM has a special loading order, other layers use the standard N, C, H, W ordering.
LSTMLayer <T> lstmLayer = null;
LSTMSimpleLayer <T> lstmSimpleLayer = null;
foreach (Layer <T> layer1 in m_net.layers)
{
if (layer1.layer_param.type == LayerParameter.LayerType.LSTM)
{
lstmLayer = layer1 as LSTMLayer <T>;
m_lstmType = LayerParameter.LayerType.LSTM;
break;
}
else if (layer1.layer_param.type == LayerParameter.LayerType.LSTM_SIMPLE)
{
lstmSimpleLayer = layer1 as LSTMSimpleLayer <T>;
m_lstmType = LayerParameter.LayerType.LSTM_SIMPLE;
break;
}
}
if (lstmLayer == null && lstmSimpleLayer == null)
{
throw new Exception("Could not find the required LSTM or LSTM_SIMPLE layer!");
}
if (m_phaseOnRun != Phase.NONE && m_phaseOnRun != Phase.RUN && strOutputBlob != null)
{
if ((m_blobOutput = m_net.FindBlob(strOutputBlob)) == null)
{
throw new Exception("Could not find the 'Output' layer top named '" + strOutputBlob + "'!");
}
}
if ((m_blobData = m_net.FindBlob("data")) == null)
{
throw new Exception("Could not find the 'Input' layer top named 'data'!");
}
if ((m_blobClip = m_net.FindBlob("clip")) == null)
{
throw new Exception("Could not find the 'Input' layer top named 'clip'!");
}
Layer <T> layer = m_net.FindLastLayer(LayerParameter.LayerType.INNERPRODUCT);
m_mycaffe.Log.CHECK(layer != null, "Could not find an ending INNERPRODUCT layer!");
if (!m_bDisableVocabulary)
{
m_nVocabSize = (int)layer.layer_param.inner_product_param.num_output;
if (rgVocabulary != null)
{
m_mycaffe.Log.CHECK_EQ(m_nVocabSize, rgVocabulary.Count, "The vocabulary count = '" + rgVocabulary.Count.ToString() + "' and last inner product output count = '" + m_nVocabSize.ToString() + "' - these do not match but they should!");
}
}
if (m_lstmType == LayerParameter.LayerType.LSTM)
{
m_nSequenceLength = m_blobData.shape(0);
m_nBatchSize = m_blobData.shape(1);
}
else
{
m_nBatchSize = (int)lstmSimpleLayer.layer_param.lstm_simple_param.batch_size;
m_nSequenceLength = m_blobData.shape(0) / m_nBatchSize;
if (phase == Phase.RUN)
{
m_nBatchSize = 1;
List <int> rgNewShape = new List <int>()
{
m_nSequenceLength, 1
};
m_blobData.Reshape(rgNewShape);
m_blobClip.Reshape(rgNewShape);
m_net.Reshape();
}
}
m_mycaffe.Log.CHECK_EQ(m_nSequenceLength, m_blobData.num, "The data num must equal the sequence lengh of " + m_nSequenceLength.ToString());
m_rgDataInput = new T[m_nSequenceLength * m_nBatchSize];
T[] rgClipInput = new T[m_nSequenceLength * m_nBatchSize];
m_mycaffe.Log.CHECK_EQ(rgClipInput.Length, m_blobClip.count(), "The clip count must equal the sequence length * batch size: " + rgClipInput.Length.ToString());
m_tZero = (T)Convert.ChangeType(0, typeof(T));
m_tOne = (T)Convert.ChangeType(1, typeof(T));
for (int i = 0; i < rgClipInput.Length; i++)
{
if (m_lstmType == LayerParameter.LayerType.LSTM)
{
rgClipInput[i] = (i < m_nBatchSize) ? m_tZero : m_tOne;
}
else
{
rgClipInput[i] = (i % m_nSequenceLength == 0) ? m_tZero : m_tOne;
}
}
m_blobClip.mutable_cpu_data = rgClipInput;
if (phase != Phase.RUN)
{
m_solver = mycaffe.GetInternalSolver();
m_solver.OnStart += m_solver_OnStart;
m_solver.OnTestStart += m_solver_OnTestStart;
m_solver.OnTestingIteration += m_solver_OnTestingIteration;
m_solver.OnTrainingIteration += m_solver_OnTrainingIteration;
if ((m_blobLabel = m_net.FindBlob("label")) == null)
{
throw new Exception("Could not find the 'Input' layer top named 'label'!");
}
m_nSequenceLengthLabel = m_blobLabel.count(0, 2);
m_rgLabelInput = new T[m_nSequenceLengthLabel];
m_mycaffe.Log.CHECK_EQ(m_rgLabelInput.Length, m_blobLabel.count(), "The label count must equal the label sequence length * batch size: " + m_rgLabelInput.Length.ToString());
m_mycaffe.Log.CHECK(m_nSequenceLengthLabel == m_nSequenceLength * m_nBatchSize || m_nSequenceLengthLabel == 1, "The label sqeuence length must be 1 or equal the length of the sequence: " + m_nSequenceLength.ToString());
}
}
/// <summary>
/// Initialize the gym with the specified properties.
/// </summary>
/// <param name="log">Specifies the output log to use.</param>
/// <param name="properties">Specifies the properties containing Gym specific initialization parameters.</param>
/// <remarks>
/// The AtariGym uses the following initialization properties.
/// GameRom='path to .rom file'
/// </remarks>
public void Initialize(Log log, PropertySet properties)
{
m_log = log;
if (m_ale != null)
{
m_ale.Shutdown();
m_ale = null;
}
m_ale = new ALE();
m_ale.Initialize();
m_ale.EnableDisplayScreen = false;
m_ale.EnableSound = false;
m_ale.EnableColorData = properties.GetPropertyAsBool("EnableColor", false);
m_ale.EnableRestrictedActionSet = true;
m_ale.EnableColorAveraging = true;
m_ale.AllowNegativeRewards = properties.GetPropertyAsBool("AllowNegativeRewards", false);
m_ale.EnableTerminateOnRallyEnd = properties.GetPropertyAsBool("TerminateOnRallyEnd", false);
m_ale.RandomSeed = (int)DateTime.Now.Ticks;
m_ale.RepeatActionProbability = 0.0f; // disable action repeatability
if (properties == null)
{
throw new Exception("The properties must be specified with the 'GameROM' set the the Game ROM file path.");
}
string strROM = properties.GetProperty("GameROM");
if (strROM.Contains('~'))
{
strROM = Utility.Replace(strROM, '~', ' ');
}
else
{
strROM = Utility.Replace(strROM, "[sp]", ' ');
}
if (!File.Exists(strROM))
{
throw new Exception("Could not find the game ROM file specified '" + strROM + "'!");
}
if (properties.GetPropertyAsBool("UseGrayscale", false))
{
m_ct = COLORTYPE.CT_GRAYSCALE;
}
m_bPreprocess = properties.GetPropertyAsBool("Preprocess", true);
m_bForceGray = properties.GetPropertyAsBool("ActionForceGray", false);
m_bEnableNumSkip = properties.GetPropertyAsBool("EnableNumSkip", true);
m_nFrameSkip = properties.GetPropertyAsInt("FrameSkip", -1);
m_ale.Load(strROM);
m_rgActionsRaw = m_ale.ActionSpace;
m_random = new CryptoRandom();
m_rgFrameSkip = new List <int>();
if (m_nFrameSkip < 0)
{
for (int i = 2; i < 5; i++)
{
m_rgFrameSkip.Add(i);
}
}
else
{
m_rgFrameSkip.Add(m_nFrameSkip);
}
m_rgActions.Add(ACTION.ACT_PLAYER_A_LEFT.ToString(), (int)ACTION.ACT_PLAYER_A_LEFT);
m_rgActions.Add(ACTION.ACT_PLAYER_A_RIGHT.ToString(), (int)ACTION.ACT_PLAYER_A_RIGHT);
if (!properties.GetPropertyAsBool("EnableBinaryActions", false))
{
m_rgActions.Add(ACTION.ACT_PLAYER_A_FIRE.ToString(), (int)ACTION.ACT_PLAYER_A_FIRE);
}
m_rgActionSet = m_rgActions.ToList();
Reset(false);
}
/// <summary>
/// The constructor.
/// </summary>
/// <param name="mycaffe">Specifies the instance of MyCaffe assoiated with the open project - when using more than one Brain, this is the master project.</param>
/// <param name="properties">Specifies the properties passed into the trainer.</param>
/// <param name="random">Specifies the random number generator used.</param>
/// <param name="phase">Specifies the phase under which to run.</param>
public Brain(MyCaffeControl <T> mycaffe, PropertySet properties, CryptoRandom random, Phase phase)
{
m_mycaffe = mycaffe;
m_solver = mycaffe.GetInternalSolver();
m_netOutput = mycaffe.GetInternalNet(phase);
m_netTarget = new Net <T>(m_mycaffe.Cuda, m_mycaffe.Log, m_netOutput.net_param, m_mycaffe.CancelEvent, null, phase);
m_properties = properties;
m_random = random;
Blob <T> data = m_netOutput.blob_by_name("data");
if (data == null)
{
m_mycaffe.Log.FAIL("Missing the expected input 'data' blob!");
}
m_nFramesPerX = data.channels;
m_nBatchSize = data.num;
Blob <T> logits = m_netOutput.blob_by_name("logits");
if (logits == null)
{
m_mycaffe.Log.FAIL("Missing the expected input 'logits' blob!");
}
m_nActionCount = logits.channels;
m_transformer = m_mycaffe.DataTransformer;
if (m_transformer == null)
{
TransformationParameter trans_param = new TransformationParameter();
int nC = m_mycaffe.CurrentProject.Dataset.TrainingSource.ImageChannels;
int nH = m_mycaffe.CurrentProject.Dataset.TrainingSource.ImageHeight;
int nW = m_mycaffe.CurrentProject.Dataset.TrainingSource.ImageWidth;
m_transformer = new DataTransformer <T>(m_mycaffe.Cuda, m_mycaffe.Log, trans_param, phase, nC, nH, nW);
}
for (int i = 0; i < m_nFramesPerX; i++)
{
m_transformer.param.mean_value.Add(255 / 2); // center each frame
}
m_transformer.param.scale = 1.0 / 255; // normalize
m_transformer.Update();
m_blobActions = new Blob <T>(m_mycaffe.Cuda, m_mycaffe.Log, false);
m_blobQValue = new Blob <T>(m_mycaffe.Cuda, m_mycaffe.Log);
m_blobNextQValue = new Blob <T>(m_mycaffe.Cuda, m_mycaffe.Log);
m_blobExpectedQValue = new Blob <T>(m_mycaffe.Cuda, m_mycaffe.Log);
m_blobDone = new Blob <T>(m_mycaffe.Cuda, m_mycaffe.Log, false);
m_blobLoss = new Blob <T>(m_mycaffe.Cuda, m_mycaffe.Log);
m_blobWeights = new Blob <T>(m_mycaffe.Cuda, m_mycaffe.Log, false);
m_fGamma = (float)properties.GetPropertyAsDouble("Gamma", m_fGamma);
m_memLoss = m_netOutput.FindLastLayer(LayerParameter.LayerType.MEMORY_LOSS) as MemoryLossLayer <T>;
if (m_memLoss == null)
{
m_mycaffe.Log.FAIL("Missing the expected MEMORY_LOSS layer!");
}
double?dfRate = mycaffe.CurrentProject.GetSolverSettingAsNumeric("base_lr");
if (dfRate.HasValue)
{
m_dfLearningRate = dfRate.Value;
}
m_nMiniBatch = m_properties.GetPropertyAsInt("MiniBatch", m_nMiniBatch);
m_bUseAcceleratedTraining = properties.GetPropertyAsBool("UseAcceleratedTraining", false);
if (m_nMiniBatch > 1)
{
m_colAccumulatedGradients = m_netOutput.learnable_parameters.Clone();
m_colAccumulatedGradients.SetDiff(0);
}
}
