/// <summary>
/// Sends the Agent info to the linked Brain.
/// </summary>
void SendInfoToBrain()
{
if (m_Brain == null)
{
return;
}
m_Info.storedVectorActions = m_Action.vectorActions;
m_ActionMasker.ResetMask();
UpdateSensors();
using (TimerStack.Instance.Scoped("CollectObservations"))
{
CollectObservations();
}
m_Info.actionMasks = m_ActionMasker.GetMask();
m_Info.reward = m_Reward;
m_Info.done = m_Done;
m_Info.maxStepReached = m_MaxStepReached;
m_Info.id = m_Id;
m_Brain.RequestDecision(m_Info, sensors, UpdateAgentAction);
if (m_Recorder != null && m_Recorder.record && Application.isEditor)
{
m_Recorder.WriteExperience(m_Info, sensors);
}
}
/// <summary>
/// Sends the Agent info to the linked Brain.
/// </summary>
void SendInfoToBrain()
{
if (m_Brain == null)
{
return;
}
m_Info.memories = m_Action.memories;
m_Info.storedVectorActions = m_Action.vectorActions;
m_Info.storedTextActions = m_Action.textActions;
m_Info.vectorObservation.Clear();
m_Info.compressedObservations.Clear();
m_ActionMasker.ResetMask();
using (TimerStack.Instance.Scoped("CollectObservations"))
{
CollectObservations();
}
m_Info.actionMasks = m_ActionMasker.GetMask();
var param = m_PolicyFactory.brainParameters;
if (m_Info.vectorObservation.Count != param.vectorObservationSize)
{
throw new UnityAgentsException(string.Format(
"Vector Observation size mismatch in continuous " +
"agent {0}. " +
"Was Expecting {1} but received {2}. ",
gameObject.name,
param.vectorObservationSize,
m_Info.vectorObservation.Count));
}
Utilities.ShiftLeft(m_Info.stackedVectorObservation, param.vectorObservationSize);
Utilities.ReplaceRange(m_Info.stackedVectorObservation, m_Info.vectorObservation,
m_Info.stackedVectorObservation.Count - m_Info.vectorObservation.Count);
m_Info.reward = m_Reward;
m_Info.done = m_Done;
m_Info.maxStepReached = m_MaxStepReached;
m_Info.id = m_Id;
m_Brain.RequestDecision(this);
if (m_Recorder != null && m_Recorder.record && Application.isEditor)
{
// This is a bit of a hack - if we're in inference mode, compressed observations won't be generated
// But we need these to be generated for the recorder. So generate them here.
if (m_Info.compressedObservations.Count == 0)
{
GenerateSensorData();
}
m_Recorder.WriteExperience(m_Info);
}
m_Info.textObservation = "";
}
void NotifyAgentDone(bool maxStepReached = false)
{
m_Info.reward = m_Reward;
m_Info.done = true;
m_Info.maxStepReached = maxStepReached;
// Request the last decision with no callbacks
// We request a decision so Python knows the Agent is done immediately
m_Brain?.RequestDecision(m_Info, sensors);
if (m_Recorder != null && m_Recorder.record && Application.isEditor)
{
m_Recorder.WriteExperience(m_Info, sensors);
}
UpdateRewardStats();
// The Agent is done, so we give it a new episode Id
m_EpisodeId = EpisodeIdCounter.GetEpisodeId();
m_Reward = 0f;
m_CumulativeReward = 0f;
m_RequestAction = false;
m_RequestDecision = false;
}
/// <summary>
/// Sends the Agent info to the linked Brain.
/// </summary>
void SendInfoToBrain()
{
if (m_Brain == null)
{
return;
}
m_Info.storedVectorActions = m_Action.vectorActions;
m_ActionMasker.ResetMask();
UpdateSensors();
using (TimerStack.Instance.Scoped("CollectObservations"))
{
CollectObservations();
}
m_Info.actionMasks = m_ActionMasker.GetMask();
// var param = m_PolicyFactory.brainParameters; // look, no brain params!
m_Info.reward = m_Reward;
m_Info.done = m_Done;
m_Info.maxStepReached = m_MaxStepReached;
m_Info.id = m_Id;
m_Brain.RequestDecision(m_Info, sensors, UpdateAgentAction);
if (m_Recorder != null && m_Recorder.record && Application.isEditor)
{
if (m_VectorSensorBuffer == null)
{
// Create a buffer for writing uncompressed (i.e. float) sensor data to
m_VectorSensorBuffer = new float[sensors.GetSensorFloatObservationSize()];
}
// This is a bit of a hack - if we're in inference mode, observations won't be generated
// But we need these to be generated for the recorder. So generate them here.
var observations = new List <Observation>();
GenerateSensorData(sensors, m_VectorSensorBuffer, m_WriteAdapter, observations);
m_Recorder.WriteExperience(m_Info, observations);
}
}
/// <summary>
/// Sends the Agent info to the linked Brain.
/// </summary>
void SendInfoToBrain()
{
if (m_Brain == null)
{
return;
}
m_Info.storedVectorActions = m_Action.vectorActions;
m_Info.observations.Clear();
m_ActionMasker.ResetMask();
UpdateSensors();
using (TimerStack.Instance.Scoped("CollectObservations"))
{
CollectObservations();
}
m_Info.actionMasks = m_ActionMasker.GetMask();
// var param = m_PolicyFactory.brainParameters; // look, no brain params!
m_Info.reward = m_Reward;
m_Info.done = m_Done;
m_Info.maxStepReached = m_MaxStepReached;
m_Info.id = m_Id;
m_Brain.RequestDecision(this);
if (m_Recorder != null && m_Recorder.record && Application.isEditor)
{
// This is a bit of a hack - if we're in inference mode, observations won't be generated
// But we need these to be generated for the recorder. So generate them here.
if (m_Info.observations.Count == 0)
{
GenerateSensorData();
}
m_Recorder.WriteExperience(m_Info);
}
}
/// <summary>
/// Sends the Agent info to the linked Brain.
/// </summary>
void SendInfoToBrain()
{
if (brain == null)
{
return;
}
info.memories = action.memories;
info.storedVectorActions = action.vectorActions;
info.storedTextActions = action.textActions;
info.vectorObservation.Clear();
actionMasker.ResetMask();
CollectObservations();
info.actionMasks = actionMasker.GetMask();
BrainParameters param = brain.brainParameters;
if (info.vectorObservation.Count != param.vectorObservationSize)
{
throw new UnityAgentsException(string.Format(
"Vector Observation size mismatch between continuous " +
"agent {0} and brain {1}. " +
"Was Expecting {2} but received {3}. ",
gameObject.name, brain.name,
brain.brainParameters.vectorObservationSize,
info.vectorObservation.Count));
}
info.stackedVectorObservation.RemoveRange(
0, param.vectorObservationSize);
info.stackedVectorObservation.AddRange(info.vectorObservation);
info.visualObservations.Clear();
var visualObservationCount = agentParameters.agentCameras.Count + agentParameters.agentRenderTextures.Count;
if (param.cameraResolutions.Length > visualObservationCount)
{
throw new UnityAgentsException(string.Format(
"Not enough cameras/renderTextures for agent {0} : Brain {1} expecting at " +
"least {2} cameras/renderTextures but only {3} were present.",
gameObject.name, brain.name,
brain.brainParameters.cameraResolutions.Length,
visualObservationCount));
}
//First add all cameras
for (int i = 0; i < agentParameters.agentCameras.Count; i++)
{
ObservationToTexture(
agentParameters.agentCameras[i],
param.cameraResolutions[i].width,
param.cameraResolutions[i].height,
ref textureArray[i]);
info.visualObservations.Add(textureArray[i]);
}
//Then add all renderTextures
var camCount = agentParameters.agentCameras.Count;
for (int i = 0; i < agentParameters.agentRenderTextures.Count; i++)
{
ObservationToTexture(
agentParameters.agentRenderTextures[i],
param.cameraResolutions[camCount + i].width,
param.cameraResolutions[camCount + i].height,
ref textureArray[i]);
info.visualObservations.Add(textureArray[i]);
}
info.reward = reward;
info.done = done;
info.maxStepReached = maxStepReached;
info.id = id;
brain.SendState(this, info);
if (recorder != null && recorder.record && Application.isEditor)
{
recorder.WriteExperience(info);
}
info.textObservation = "";
}
/// <summary>
/// Sends the Agent info to the linked Brain.
/// </summary>
void SendInfoToBrain()
{
if (brain == null)
{
return;
}
m_Info.memories = m_Action.memories;
m_Info.storedVectorActions = m_Action.vectorActions;
m_Info.storedTextActions = m_Action.textActions;
m_Info.vectorObservation.Clear();
m_ActionMasker.ResetMask();
CollectObservations();
m_Info.actionMasks = m_ActionMasker.GetMask();
var param = brain.brainParameters;
if (m_Info.vectorObservation.Count != param.vectorObservationSize)
{
throw new UnityAgentsException(string.Format(
"Vector Observation size mismatch between continuous " +
"agent {0} and brain {1}. " +
"Was Expecting {2} but received {3}. ",
gameObject.name, brain.name,
brain.brainParameters.vectorObservationSize,
m_Info.vectorObservation.Count));
}
Utilities.ShiftLeft(m_Info.stackedVectorObservation, param.vectorObservationSize);
Utilities.ReplaceRange(m_Info.stackedVectorObservation, m_Info.vectorObservation,
m_Info.stackedVectorObservation.Count - m_Info.vectorObservation.Count);
m_Info.visualObservations.Clear();
var visualObservationCount = agentParameters.agentCameras.Count + agentParameters.agentRenderTextures.Count;
if (param.cameraResolutions.Length > visualObservationCount)
{
throw new UnityAgentsException(string.Format(
"Not enough cameras/renderTextures for agent {0} : Brain {1} expecting at " +
"least {2} cameras/renderTextures but only {3} were present.",
gameObject.name, brain.name,
brain.brainParameters.cameraResolutions.Length,
visualObservationCount));
}
//First add all cameras
for (var i = 0; i < agentParameters.agentCameras.Count; i++)
{
var obsTexture = ObservationToTexture(
agentParameters.agentCameras[i],
param.cameraResolutions[i].width,
param.cameraResolutions[i].height);
m_Info.visualObservations.Add(obsTexture);
}
//Then add all renderTextures
var camCount = agentParameters.agentCameras.Count;
for (var i = 0; i < agentParameters.agentRenderTextures.Count; i++)
{
var obsTexture = ObservationToTexture(
agentParameters.agentRenderTextures[i],
param.cameraResolutions[camCount + i].width,
param.cameraResolutions[camCount + i].height);
m_Info.visualObservations.Add(obsTexture);
}
m_Info.reward = m_Reward;
m_Info.done = m_Done;
m_Info.maxStepReached = m_MaxStepReached;
m_Info.id = m_Id;
brain.SendState(this, m_Info);
if (m_Recorder != null && m_Recorder.record)
{
m_Recorder.WriteExperience(m_Info);
}
m_Info.textObservation = "";
}
