您最多选择25个主题
主题必须以中文或者字母或数字开头，可以包含连字符 (-)，并且长度不得超过35个字符
783 行
29 KiB
783 行
29 KiB
using System;
|
|
using System.Collections.Generic;
|
|
using UnityEngine;
|
|
using Barracuda;
|
|
using MLAgents.Sensors;
|
|
using MLAgents.Demonstrations;
|
|
using MLAgents.Policies;
|
|
|
|
namespace MLAgents
|
|
{
|
|
/// <summary>
|
|
/// Struct that contains all the information for an Agent, including its
|
|
/// observations, actions and current status.
|
|
/// </summary>
|
|
internal struct AgentInfo
|
|
{
|
|
/// <summary>
|
|
/// Keeps track of the last vector action taken by the Brain.
|
|
/// </summary>
|
|
public float[] storedVectorActions;
|
|
|
|
/// <summary>
|
|
/// For discrete control, specifies the actions that the agent cannot take. Is true if
|
|
/// the action is masked.
|
|
/// </summary>
|
|
public bool[] discreteActionMasks;
|
|
|
|
/// <summary>
|
|
/// Current agent reward.
|
|
/// </summary>
|
|
public float reward;
|
|
|
|
/// <summary>
|
|
/// Whether the agent is done or not.
|
|
/// </summary>
|
|
public bool done;
|
|
|
|
/// <summary>
|
|
/// Whether the agent has reached its max step count for this episode.
|
|
/// </summary>
|
|
public bool maxStepReached;
|
|
|
|
/// <summary>
|
|
/// Episode identifier each agent receives at every reset. It is used
|
|
/// to separate between different agents in the environment.
|
|
/// </summary>
|
|
public int episodeId;
|
|
}
|
|
|
|
/// <summary>
|
|
/// Struct that contains the action information sent from the Brain to the
|
|
/// Agent.
|
|
/// </summary>
|
|
internal struct AgentAction
|
|
{
|
|
public float[] vectorActions;
|
|
}
|
|
|
|
/// <summary>
|
|
/// Agent MonoBehaviour class that is attached to a Unity GameObject, making it
|
|
/// an Agent. An agent produces observations and takes actions in the
|
|
/// environment. Observations are determined by the cameras attached
|
|
/// to the agent in addition to the vector observations implemented by the
|
|
/// user in <see cref="Agent.CollectObservations(VectorSensor)"/>.
|
|
/// On the other hand, actions are determined by decisions produced by a Policy.
|
|
/// Currently, this class is expected to be extended to implement the desired agent behavior.
|
|
/// </summary>
|
|
/// <remarks>
|
|
/// Simply speaking, an agent roams through an environment and at each step
|
|
/// of the environment extracts its current observation, sends them to its
|
|
/// policy and in return receives an action. In practice,
|
|
/// however, an agent need not send its observation at every step since very
|
|
/// little may have changed between successive steps.
|
|
///
|
|
/// At any step, an agent may be considered done due to a variety of reasons:
|
|
/// - The agent reached an end state within its environment.
|
|
/// - The agent reached the maximum # of steps (i.e. timed out).
|
|
/// - The academy reached the maximum # of steps (forced agent to be done).
|
|
///
|
|
/// Here, an agent reaches an end state if it completes its task successfully
|
|
/// or somehow fails along the way. In the case where an agent is done before
|
|
/// the academy, it either resets and restarts, or just lingers until the
|
|
/// academy is done.
|
|
///
|
|
/// An important note regarding steps and episodes is due. Here, an agent step
|
|
/// corresponds to an academy step, which also corresponds to Unity
|
|
/// environment step (i.e. each FixedUpdate call). This is not the case for
|
|
/// episodes. The academy controls the global episode count and each agent
|
|
/// controls its own local episode count and can reset and start a new local
|
|
/// episode independently (based on its own experience). Thus an academy
|
|
/// (global) episode can be viewed as the upper-bound on an agents episode
|
|
/// length and that within a single global episode, an agent may have completed
|
|
/// multiple local episodes. Consequently, if an agent max step is
|
|
/// set to a value larger than the academy max steps value, then the academy
|
|
/// value takes precedence (since the agent max step will never be reached).
|
|
///
|
|
/// Lastly, note that at any step the policy to the agent is allowed to
|
|
/// change model with <see cref="GiveModel"/>.
|
|
///
|
|
/// Implementation-wise, it is required that this class is extended and the
|
|
/// virtual methods overridden. For sample implementations of agent behavior,
|
|
/// see the Examples/ directory within this Unity project.
|
|
/// </remarks>
|
|
[HelpURL("https://github.com/Unity-Technologies/ml-agents/blob/master/" +
|
|
"docs/Learning-Environment-Design-Agents.md")]
|
|
[Serializable]
|
|
[RequireComponent(typeof(BehaviorParameters))]
|
|
public class Agent : MonoBehaviour, ISerializationCallbackReceiver
|
|
{
|
|
IPolicy m_Brain;
|
|
BehaviorParameters m_PolicyFactory;
|
|
|
|
/// This code is here to make the upgrade path for users using maxStep
|
|
/// easier. We will hook into the Serialization code and make sure that
|
|
/// agentParameters.maxStep and this.maxStep are in sync.
|
|
[Serializable]
|
|
internal struct AgentParameters
|
|
{
|
|
public int maxStep;
|
|
}
|
|
|
|
/// <summary>
|
|
/// The team ID for this Agent.
|
|
/// </summary>
|
|
public int TeamId
|
|
{
|
|
get
|
|
{
|
|
LazyInitialize();
|
|
return m_PolicyFactory.TeamId;
|
|
}
|
|
}
|
|
|
|
/// <summary>
|
|
/// The name of the behavior of the Agent.
|
|
/// </summary>
|
|
public string BehaviorName
|
|
{
|
|
get
|
|
{
|
|
LazyInitialize();
|
|
return m_PolicyFactory.behaviorName;
|
|
}
|
|
}
|
|
|
|
[SerializeField][HideInInspector]
|
|
internal AgentParameters agentParameters;
|
|
[SerializeField][HideInInspector]
|
|
internal bool hasUpgradedFromAgentParameters;
|
|
|
|
/// <summary>
|
|
/// The maximum number of steps the agent takes before being done.
|
|
/// </summary>
|
|
/// <remarks>
|
|
/// If set to 0, the agent can only be set to done programmatically (or
|
|
/// when the Academy is done).
|
|
/// If set to any positive integer, the agent will be set to done after
|
|
/// that many steps. Note that setting the max step to a value greater
|
|
/// than the academy max step value renders it useless.
|
|
/// </remarks>
|
|
[HideInInspector] public int maxStep;
|
|
|
|
/// Current Agent information (message sent to Brain).
|
|
AgentInfo m_Info;
|
|
|
|
/// Current Agent action (message sent from Brain).
|
|
AgentAction m_Action;
|
|
|
|
/// Represents the reward the agent accumulated during the current step.
|
|
/// It is reset to 0 at the beginning of every step.
|
|
/// Should be set to a positive value when the agent performs a "good"
|
|
/// action that we wish to reinforce/reward, and set to a negative value
|
|
/// when the agent performs a "bad" action that we wish to punish/deter.
|
|
/// Additionally, the magnitude of the reward should not exceed 1.0
|
|
float m_Reward;
|
|
|
|
/// Keeps track of the cumulative reward in this episode.
|
|
float m_CumulativeReward;
|
|
|
|
/// Whether or not the agent requests an action.
|
|
bool m_RequestAction;
|
|
|
|
/// Whether or not the agent requests a decision.
|
|
bool m_RequestDecision;
|
|
|
|
/// Keeps track of the number of steps taken by the agent in this episode.
|
|
/// Note that this value is different for each agent, and may not overlap
|
|
/// with the step counter in the Academy, since agents reset based on
|
|
/// their own experience.
|
|
int m_StepCount;
|
|
|
|
/// Episode identifier each agent receives. It is used
|
|
/// to separate between different agents in the environment.
|
|
/// This Id will be changed every time the Agent resets.
|
|
int m_EpisodeId;
|
|
|
|
/// Whether or not the Agent has been initialized already
|
|
bool m_Initialized;
|
|
|
|
/// Keeps track of the actions that are masked at each step.
|
|
DiscreteActionMasker m_ActionMasker;
|
|
|
|
/// <summary>
|
|
/// Set of DemonstrationWriters that the Agent will write its step information to.
|
|
/// If you use a DemonstrationRecorder component, this will automatically register its DemonstrationWriter.
|
|
/// You can also add your own DemonstrationWriter by calling
|
|
/// DemonstrationRecorder.AddDemonstrationWriterToAgent()
|
|
/// </summary>
|
|
internal ISet<DemonstrationWriter> DemonstrationWriters = new HashSet<DemonstrationWriter>();
|
|
|
|
/// <summary>
|
|
/// List of sensors used to generate observations.
|
|
/// Currently generated from attached SensorComponents, and a legacy VectorSensor
|
|
/// </summary>
|
|
internal List<ISensor> sensors;
|
|
|
|
/// <summary>
|
|
/// VectorSensor which is written to by AddVectorObs
|
|
/// </summary>
|
|
internal VectorSensor collectObservationsSensor;
|
|
|
|
/// <summary>
|
|
/// Called when the attached <see cref="GameObject"/> becomes enabled and active.
|
|
/// </summary>
|
|
protected virtual void OnEnable()
|
|
{
|
|
LazyInitialize();
|
|
}
|
|
|
|
/// <summary>
|
|
/// <inheritdoc cref="OnBeforeSerialize"/>
|
|
/// </summary>
|
|
public void OnBeforeSerialize()
|
|
{
|
|
// Manages a serialization upgrade issue from v0.13 to v0.14 where maxStep moved
|
|
// from AgentParameters (since removed) to Agent
|
|
if (maxStep == 0 && maxStep != agentParameters.maxStep && !hasUpgradedFromAgentParameters)
|
|
{
|
|
maxStep = agentParameters.maxStep;
|
|
}
|
|
hasUpgradedFromAgentParameters = true;
|
|
}
|
|
|
|
/// <summary>
|
|
/// <inheritdoc cref="OnAfterDeserialize"/>
|
|
/// </summary>
|
|
public void OnAfterDeserialize()
|
|
{
|
|
// Manages a serialization upgrade issue from v0.13 to v0.14 where maxStep moved
|
|
// from AgentParameters (since removed) to Agent
|
|
if (maxStep == 0 && maxStep != agentParameters.maxStep && !hasUpgradedFromAgentParameters)
|
|
{
|
|
maxStep = agentParameters.maxStep;
|
|
}
|
|
hasUpgradedFromAgentParameters = true;
|
|
}
|
|
|
|
/// <summary>
|
|
/// Initializes the agent. Can be safely called multiple times.
|
|
/// </summary>
|
|
public void LazyInitialize()
|
|
{
|
|
if (m_Initialized)
|
|
{
|
|
return;
|
|
}
|
|
m_Initialized = true;
|
|
|
|
// Grab the "static" properties for the Agent.
|
|
m_EpisodeId = EpisodeIdCounter.GetEpisodeId();
|
|
m_PolicyFactory = GetComponent<BehaviorParameters>();
|
|
|
|
m_Info = new AgentInfo();
|
|
m_Action = new AgentAction();
|
|
sensors = new List<ISensor>();
|
|
|
|
Academy.Instance.AgentIncrementStep += AgentIncrementStep;
|
|
Academy.Instance.AgentSendState += SendInfo;
|
|
Academy.Instance.DecideAction += DecideAction;
|
|
Academy.Instance.AgentAct += AgentStep;
|
|
Academy.Instance.AgentForceReset += _AgentReset;
|
|
m_Brain = m_PolicyFactory.GeneratePolicy(Heuristic);
|
|
ResetData();
|
|
InitializeAgent();
|
|
InitializeSensors();
|
|
}
|
|
|
|
/// <summary>
|
|
/// Reason that the Agent is being considered "done"
|
|
/// </summary>
|
|
enum DoneReason
|
|
{
|
|
/// <summary>
|
|
/// The <see cref="Done"/> method was called.
|
|
/// </summary>
|
|
DoneCalled,
|
|
|
|
/// <summary>
|
|
/// The max steps for the Agent were reached.
|
|
/// </summary>
|
|
MaxStepReached,
|
|
|
|
/// <summary>
|
|
/// The Agent was disabled
|
|
/// </summary>
|
|
Disabled,
|
|
}
|
|
|
|
/// <summary>
|
|
/// Called when the attached <see cref="GameObject"/> becomes disabled and inactive.
|
|
/// </summary>
|
|
protected virtual void OnDisable()
|
|
{
|
|
DemonstrationWriters.Clear();
|
|
|
|
// If Academy.Dispose has already been called, we don't need to unregister with it.
|
|
// We don't want to even try, because this will lazily create a new Academy!
|
|
if (Academy.IsInitialized)
|
|
{
|
|
Academy.Instance.AgentIncrementStep -= AgentIncrementStep;
|
|
Academy.Instance.AgentSendState -= SendInfo;
|
|
Academy.Instance.DecideAction -= DecideAction;
|
|
Academy.Instance.AgentAct -= AgentStep;
|
|
Academy.Instance.AgentForceReset -= _AgentReset;
|
|
}
|
|
NotifyAgentDone(DoneReason.Disabled);
|
|
m_Brain?.Dispose();
|
|
m_Initialized = false;
|
|
}
|
|
|
|
void NotifyAgentDone(DoneReason doneReason)
|
|
{
|
|
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>
|
|
/// Updates the Model for the agent. Any model currently assigned to the
|
|
/// agent will be replaced with the provided one. If the arguments are
|
|
/// identical to the current parameters of the agent, the model will
|
|
/// remain unchanged.
|
|
/// </summary>
|
|
/// <param name="behaviorName"> The identifier of the behavior. This
|
|
/// will categorize the agent when training.
|
|
/// </param>
|
|
/// <param name="model"> The model to use for inference.</param>
|
|
/// <param name = "inferenceDevice"> Define on what device the model
|
|
/// will be run.</param>
|
|
public void GiveModel(
|
|
string behaviorName,
|
|
NNModel model,
|
|
InferenceDevice inferenceDevice = InferenceDevice.CPU)
|
|
{
|
|
m_PolicyFactory.GiveModel(behaviorName, model, inferenceDevice);
|
|
m_Brain?.Dispose();
|
|
m_Brain = m_PolicyFactory.GeneratePolicy(Heuristic);
|
|
}
|
|
|
|
/// <summary>
|
|
/// Updates the type of behavior for the agent.
|
|
/// </summary>
|
|
/// <param name="behaviorType"> The new behaviorType for the Agent
|
|
/// </param>
|
|
public void SetBehaviorType(BehaviorType behaviorType)
|
|
{
|
|
if (m_PolicyFactory.m_BehaviorType == behaviorType)
|
|
{
|
|
return;
|
|
}
|
|
m_PolicyFactory.m_BehaviorType = behaviorType;
|
|
m_Brain?.Dispose();
|
|
m_Brain = m_PolicyFactory.GeneratePolicy(Heuristic);
|
|
}
|
|
|
|
/// <summary>
|
|
/// Returns the current step counter (within the current episode).
|
|
/// </summary>
|
|
/// <returns>
|
|
/// Current step count.
|
|
/// </returns>
|
|
public int StepCount
|
|
{
|
|
get { return m_StepCount; }
|
|
}
|
|
|
|
/// <summary>
|
|
/// Overrides the current step reward of the agent and updates the episode
|
|
/// reward accordingly.
|
|
/// </summary>
|
|
/// <param name="reward">The new value of the reward.</param>
|
|
public void SetReward(float reward)
|
|
{
|
|
#if DEBUG
|
|
Utilities.DebugCheckNanAndInfinity(reward, nameof(reward), nameof(SetReward));
|
|
#endif
|
|
m_CumulativeReward += (reward - m_Reward);
|
|
m_Reward = reward;
|
|
}
|
|
|
|
/// <summary>
|
|
/// Increments the step and episode rewards by the provided value.
|
|
/// </summary>
|
|
/// <param name="increment">Incremental reward value.</param>
|
|
public void AddReward(float increment)
|
|
{
|
|
#if DEBUG
|
|
Utilities.DebugCheckNanAndInfinity(increment, nameof(increment), nameof(AddReward));
|
|
#endif
|
|
m_Reward += increment;
|
|
m_CumulativeReward += increment;
|
|
}
|
|
|
|
/// <summary>
|
|
/// Retrieves the episode reward for the Agent.
|
|
/// </summary>
|
|
/// <returns>The episode reward.</returns>
|
|
public float GetCumulativeReward()
|
|
{
|
|
return m_CumulativeReward;
|
|
}
|
|
|
|
void UpdateRewardStats()
|
|
{
|
|
var gaugeName = $"{m_PolicyFactory.behaviorName}.CumulativeReward";
|
|
TimerStack.Instance.SetGauge(gaugeName, GetCumulativeReward());
|
|
}
|
|
|
|
/// <summary>
|
|
/// Sets the done flag to true.
|
|
/// </summary>
|
|
public void Done()
|
|
{
|
|
NotifyAgentDone(DoneReason.DoneCalled);
|
|
_AgentReset();
|
|
}
|
|
|
|
/// <summary>
|
|
/// Is called when the agent must request the brain for a new decision.
|
|
/// </summary>
|
|
public void RequestDecision()
|
|
{
|
|
m_RequestDecision = true;
|
|
RequestAction();
|
|
}
|
|
|
|
/// <summary>
|
|
/// Is called then the agent must perform a new action.
|
|
/// </summary>
|
|
public void RequestAction()
|
|
{
|
|
m_RequestAction = true;
|
|
}
|
|
|
|
/// Helper function that resets all the data structures associated with
|
|
/// the agent. Typically used when the agent is being initialized or reset
|
|
/// at the end of an episode.
|
|
void ResetData()
|
|
{
|
|
var param = m_PolicyFactory.brainParameters;
|
|
m_ActionMasker = new DiscreteActionMasker(param);
|
|
// If we haven't initialized vectorActions, initialize to 0. This should only
|
|
// happen during the creation of the Agent. In subsequent episodes, vectorAction
|
|
// should stay the previous action before the Done(), so that it is properly recorded.
|
|
if (m_Action.vectorActions == null)
|
|
{
|
|
m_Action.vectorActions = new float[param.numActions];
|
|
m_Info.storedVectorActions = new float[param.numActions];
|
|
}
|
|
}
|
|
|
|
/// <summary>
|
|
/// Initializes the agent, called once when the agent is enabled. Can be
|
|
/// left empty if there is no special, unique set-up behavior for the
|
|
/// agent.
|
|
/// </summary>
|
|
/// <remarks>
|
|
/// One sample use is to store local references to other objects in the
|
|
/// scene which would facilitate computing this agents observation.
|
|
/// </remarks>
|
|
public virtual void InitializeAgent()
|
|
{
|
|
}
|
|
|
|
/// <summary>
|
|
/// When the Agent uses Heuristics, it will call this method every time it
|
|
/// needs an action. This can be used for debugging or controlling the agent
|
|
/// with keyboard.
|
|
/// </summary>
|
|
/// <returns> A float array corresponding to the next action of the Agent
|
|
/// </returns>
|
|
public virtual float[] Heuristic()
|
|
{
|
|
Debug.LogWarning("Heuristic method called but not implemented. Returning placeholder actions.");
|
|
var param = m_PolicyFactory.brainParameters;
|
|
|
|
return new float[param.numActions];
|
|
}
|
|
|
|
/// <summary>
|
|
/// Set up the list of ISensors on the Agent. By default, this will select any
|
|
/// SensorBase's attached to the Agent.
|
|
/// </summary>
|
|
internal void InitializeSensors()
|
|
{
|
|
// Get all attached sensor components
|
|
SensorComponent[] attachedSensorComponents;
|
|
if (m_PolicyFactory.useChildSensors)
|
|
{
|
|
attachedSensorComponents = GetComponentsInChildren<SensorComponent>();
|
|
}
|
|
else
|
|
{
|
|
attachedSensorComponents = GetComponents<SensorComponent>();
|
|
}
|
|
|
|
sensors.Capacity += attachedSensorComponents.Length;
|
|
foreach (var component in attachedSensorComponents)
|
|
{
|
|
sensors.Add(component.CreateSensor());
|
|
}
|
|
|
|
// Support legacy CollectObservations
|
|
var param = m_PolicyFactory.brainParameters;
|
|
if (param.vectorObservationSize > 0)
|
|
{
|
|
collectObservationsSensor = new VectorSensor(param.vectorObservationSize);
|
|
if (param.numStackedVectorObservations > 1)
|
|
{
|
|
var stackingSensor = new StackingSensor(
|
|
collectObservationsSensor, param.numStackedVectorObservations);
|
|
sensors.Add(stackingSensor);
|
|
}
|
|
else
|
|
{
|
|
sensors.Add(collectObservationsSensor);
|
|
}
|
|
}
|
|
|
|
// Sort the Sensors by name to ensure determinism
|
|
sensors.Sort((x, y) => x.GetName().CompareTo(y.GetName()));
|
|
|
|
#if DEBUG
|
|
// Make sure the names are actually unique
|
|
for (var i = 0; i < sensors.Count - 1; i++)
|
|
{
|
|
Debug.Assert(
|
|
!sensors[i].GetName().Equals(sensors[i + 1].GetName()),
|
|
"Sensor names must be unique.");
|
|
}
|
|
#endif
|
|
}
|
|
|
|
/// <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;
|
|
}
|
|
|
|
m_Info.storedVectorActions = m_Action.vectorActions;
|
|
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 UpdateSensors()
|
|
{
|
|
foreach (var sensor in sensors)
|
|
{
|
|
sensor.Update();
|
|
}
|
|
}
|
|
|
|
/// <summary>
|
|
/// Collects the vector observations of the agent.
|
|
/// The agent observation describes the current environment from the
|
|
/// perspective of the agent.
|
|
/// </summary>
|
|
/// <param name="sensor">
|
|
/// The vector observations for the agent.
|
|
/// </param>
|
|
/// <remarks>
|
|
/// An agents observation is any environment information that helps
|
|
/// the Agent achieve its goal. For example, for a fighting Agent, its
|
|
/// observation could include distances to friends or enemies, or the
|
|
/// current level of ammunition at its disposal.
|
|
/// Recall that an Agent may attach vector or visual observations.
|
|
/// Vector observations are added by calling the provided helper methods
|
|
/// on the VectorSensor input:
|
|
/// - <see cref="VectorSensor.AddObservation(int)"/>
|
|
/// - <see cref="VectorSensor.AddObservation(float)"/>
|
|
/// - <see cref="VectorSensor.AddObservation(Vector3)"/>
|
|
/// - <see cref="VectorSensor.AddObservation(Vector2)"/>
|
|
/// - <see cref="VectorSensor.AddObservation(Quaternion)"/>
|
|
/// - <see cref="VectorSensor.AddObservation(bool)"/>
|
|
/// - <see cref="VectorSensor.AddObservation(IEnumerable{float})"/>
|
|
/// - <see cref="VectorSensor.AddOneHotObservation(int, int)"/>
|
|
/// Depending on your environment, any combination of these helpers can
|
|
/// be used. They just need to be used in the exact same order each time
|
|
/// this method is called and the resulting size of the vector observation
|
|
/// needs to match the vectorObservationSize attribute of the linked Brain.
|
|
/// Visual observations are implicitly added from the cameras attached to
|
|
/// the Agent.
|
|
/// </remarks>
|
|
public virtual void CollectObservations(VectorSensor sensor)
|
|
{
|
|
}
|
|
|
|
/// <summary>
|
|
/// Collects the masks for discrete actions.
|
|
/// When using discrete actions, the agent will not perform the masked action.
|
|
/// </summary>
|
|
/// <param name="actionMasker">
|
|
/// The action masker for the agent.
|
|
/// </param>
|
|
/// <remarks>
|
|
/// When using Discrete Control, you can prevent the Agent from using a certain
|
|
/// action by masking it with <see cref="DiscreteActionMasker.SetMask(int, IEnumerable{int})"/>
|
|
/// </remarks>
|
|
public virtual void CollectDiscreteActionMasks(DiscreteActionMasker actionMasker)
|
|
{
|
|
}
|
|
|
|
/// <summary>
|
|
/// Specifies the agent behavior at every step based on the provided
|
|
/// action.
|
|
/// </summary>
|
|
/// <param name="vectorAction">
|
|
/// Vector action. Note that for discrete actions, the provided array
|
|
/// will be of length 1.
|
|
/// </param>
|
|
public virtual void AgentAction(float[] vectorAction)
|
|
{
|
|
}
|
|
|
|
/// <summary>
|
|
/// Specifies the agent behavior when being reset, which can be due to
|
|
/// the agent or Academy being done (i.e. completion of local or global
|
|
/// episode).
|
|
/// </summary>
|
|
public virtual void AgentReset()
|
|
{
|
|
}
|
|
|
|
/// <summary>
|
|
/// Returns the last action that was decided on by the Agent
|
|
/// </summary>
|
|
/// <returns>
|
|
/// The last action that was decided by the Agent (or null if no decision has been made)
|
|
/// </returns>
|
|
public float[] GetAction()
|
|
{
|
|
return m_Action.vectorActions;
|
|
}
|
|
|
|
/// <summary>
|
|
/// An internal reset method that updates internal data structures in
|
|
/// addition to calling <see cref="AgentReset"/>.
|
|
/// </summary>
|
|
void _AgentReset()
|
|
{
|
|
ResetData();
|
|
m_StepCount = 0;
|
|
AgentReset();
|
|
}
|
|
|
|
/// <summary>
|
|
/// Scales continuous action from [-1, 1] to arbitrary range.
|
|
/// </summary>
|
|
/// <param name="rawAction"></param>
|
|
/// <param name="min"></param>
|
|
/// <param name="max"></param>
|
|
/// <returns></returns>
|
|
protected static float ScaleAction(float rawAction, float min, float max)
|
|
{
|
|
var middle = (min + max) / 2;
|
|
var range = (max - min) / 2;
|
|
return rawAction * range + middle;
|
|
}
|
|
|
|
/// <summary>
|
|
/// Signals the agent that it must sent its decision to the brain.
|
|
/// </summary>
|
|
void SendInfo()
|
|
{
|
|
// If the Agent is done, it has just reset and thus requires a new decision
|
|
if (m_RequestDecision)
|
|
{
|
|
SendInfoToBrain();
|
|
m_Reward = 0f;
|
|
m_RequestDecision = false;
|
|
}
|
|
}
|
|
|
|
void AgentIncrementStep()
|
|
{
|
|
m_StepCount += 1;
|
|
}
|
|
|
|
/// Used by the brain to make the agent perform a step.
|
|
void AgentStep()
|
|
{
|
|
if ((m_RequestAction) && (m_Brain != null))
|
|
{
|
|
m_RequestAction = false;
|
|
AgentAction(m_Action.vectorActions);
|
|
}
|
|
|
|
if ((m_StepCount >= maxStep) && (maxStep > 0))
|
|
{
|
|
NotifyAgentDone(DoneReason.MaxStepReached);
|
|
_AgentReset();
|
|
}
|
|
}
|
|
|
|
void DecideAction()
|
|
{
|
|
m_Action.vectorActions = m_Brain?.DecideAction();
|
|
if (m_Action.vectorActions == null)
|
|
{
|
|
ResetData();
|
|
}
|
|
}
|
|
}
|
|
}
|