void NotifyAgentDone(DoneReason doneReason)
{
m_Info.episodeId = m_EpisodeId;
m_Info.reward = m_Reward;
m_Info.done = true;
m_Info.maxStepReached = doneReason == DoneReason.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);
// We also have to write any to any DemonstationStores so that they get the "done" flag.
foreach (var demoWriter in DemonstrationWriters)
{
demoWriter.Record(m_Info, sensors);
}
if (doneReason != DoneReason.Disabled)
{
// We don't want to udpate the reward stats when the Agent is disabled, because this will make
// the rewards look lower than they actually are during shutdown.
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();
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.storedVectorActions = m_Action.vectorActions;
m_ActionMasker.ResetMask();
UpdateSensors();
using (TimerStack.Instance.Scoped("CollectObservations"))
{
CollectObservations(collectObservationsSensor, m_ActionMasker);
}
m_Info.actionMasks = m_ActionMasker.GetMask();
m_Info.reward = m_Reward;
m_Info.done = false;
m_Info.maxStepReached = false;
m_Info.episodeId = m_EpisodeId;
m_Brain.RequestDecision(m_Info, sensors);
// If we have any DemonstrationWriters, write the AgentInfo and sensors to them.
foreach (var demoWriter in DemonstrationWriters)
{
demoWriter.Record(m_Info, sensors);
}
}
void NotifyAgentDone()
{
m_Info.done = true;
// Request the last decision with no callbacks
// We request a decision so Python knows the Agent is disabled
m_Brain?.RequestDecision(m_Info, sensors, (a) => { });
}
/// <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 = "";
}
/// <summary>
/// Sends the Agent info to the linked Brain.
/// </summary>
void SendInfoToBrain()
{
if (!m_Initialized)
{
throw new UnityAgentsException("Call to SendInfoToBrain when Agent hasn't been initialized." +
"Please ensure that you are calling 'base.OnEnable()' if you have overridden OnEnable.");
}
if (m_Brain == null)
{
return;
}
if (m_Info.done)
{
Array.Clear(m_Info.storedVectorActions, 0, m_Info.storedVectorActions.Length);
}
else
{
Array.Copy(m_Action.vectorActions, m_Info.storedVectorActions, m_Action.vectorActions.Length);
}
m_ActionMasker.ResetMask();
UpdateSensors();
using (TimerStack.Instance.Scoped("CollectObservations"))
{
CollectObservations(collectObservationsSensor);
}
using (TimerStack.Instance.Scoped("CollectDiscreteActionMasks"))
{
if (m_PolicyFactory.brainParameters.vectorActionSpaceType == SpaceType.Discrete)
{
CollectDiscreteActionMasks(m_ActionMasker);
}
}
m_Info.discreteActionMasks = m_ActionMasker.GetMask();
m_Info.reward = m_Reward;
m_Info.done = false;
m_Info.maxStepReached = false;
m_Info.episodeId = m_EpisodeId;
m_Brain.RequestDecision(m_Info, sensors);
// If we have any DemonstrationWriters, write the AgentInfo and sensors to them.
foreach (var demoWriter in DemonstrationWriters)
{
demoWriter.Record(m_Info, sensors);
}
}
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, (a) => {});
// 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;
}
void NotifyAgentDone(DoneReason doneReason)
{
if (m_Info.done)
{
// The Agent was already marked as Done and should not be notified again
return;
}
m_Info.episodeId = m_EpisodeId;
m_Info.reward = m_Reward;
m_Info.done = true;
m_Info.maxStepReached = doneReason == DoneReason.MaxStepReached;
if (collectObservationsSensor != null)
{
// Make sure the latest observations are being passed to training.
collectObservationsSensor.Reset();
CollectObservations(collectObservationsSensor);
}
// 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);
ResetSensors();
// We also have to write any to any DemonstationStores so that they get the "done" flag.
foreach (var demoWriter in DemonstrationWriters)
{
demoWriter.Record(m_Info, sensors);
}
if (doneReason != DoneReason.Disabled)
{
// We don't want to update the reward stats when the Agent is disabled, because this will make
// the rewards look lower than they actually are during shutdown.
m_CompletedEpisodes++;
UpdateRewardStats();
}
m_Reward = 0f;
m_CumulativeReward = 0f;
m_RequestAction = false;
m_RequestDecision = false;
Array.Clear(m_Info.storedVectorActions, 0, m_Info.storedVectorActions.Length);
}
/// <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);
}
}
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;
}
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);
// We also have to write any to any DemonstationStores so that they get the "done" flag.
foreach (var demoWriter in DemonstrationWriters)
{
demoWriter.Record(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;
}
