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
776 行
29 KiB
776 行
29 KiB
using System;
|
|
using System.Collections.Generic;
|
|
using UnityEngine;
|
|
using Barracuda;
|
|
using MLAgents.Sensor;
|
|
using UnityEngine.Serialization;
|
|
|
|
namespace MLAgents
|
|
{
|
|
/// <summary>
|
|
/// Struct that contains all the information for an Agent, including its
|
|
/// observations, actions and current status, that is sent to the Brain.
|
|
/// </summary>
|
|
public 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[] actionMasks;
|
|
|
|
/// <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 Monobehavior 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="CollectObservations"/>. 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 <see cref="m_Done"/>.
|
|
/// This could occur 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 abstract class Agent : MonoBehaviour
|
|
{
|
|
IPolicy m_Brain;
|
|
BehaviorParameters m_PolicyFactory;
|
|
|
|
/// <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.
|
|
ActionMasker m_ActionMasker;
|
|
|
|
/// <summary>
|
|
/// Demonstration recorder.
|
|
/// </summary>
|
|
DemonstrationRecorder m_Recorder;
|
|
|
|
/// <summary>
|
|
/// List of sensors used to generate observations.
|
|
/// Currently generated from attached SensorComponents, and a legacy VectorSensor
|
|
/// </summary>
|
|
[FormerlySerializedAs("m_Sensors")]
|
|
internal List<ISensor> sensors;
|
|
|
|
/// <summary>
|
|
/// VectorSensor which is written to by AddVectorObs
|
|
/// </summary>
|
|
internal VectorSensor collectObservationsSensor;
|
|
|
|
/// MonoBehaviour function that is called when the attached GameObject
|
|
/// becomes enabled or active.
|
|
void OnEnable()
|
|
{
|
|
LazyInitialize();
|
|
}
|
|
|
|
/// Helper method for the <see cref="OnEnable"/> event, created to
|
|
/// facilitate testing.
|
|
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_Recorder = GetComponent<DemonstrationRecorder>();
|
|
|
|
|
|
m_Info = new AgentInfo();
|
|
m_Action = new AgentAction();
|
|
sensors = new List<ISensor>();
|
|
|
|
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();
|
|
|
|
}
|
|
|
|
/// Monobehavior function that is called when the attached GameObject
|
|
/// becomes disabled or inactive.
|
|
void OnDisable()
|
|
{
|
|
// 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.AgentSendState -= SendInfo;
|
|
Academy.Instance.DecideAction -= DecideAction;
|
|
Academy.Instance.AgentAct -= AgentStep;
|
|
Academy.Instance.AgentForceReset -= _AgentReset;
|
|
}
|
|
NotifyAgentDone();
|
|
m_Brain?.Dispose();
|
|
m_Initialized = 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);
|
|
|
|
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>
|
|
/// Returns the current step counter (within the current episode).
|
|
/// </summary>
|
|
/// <returns>
|
|
/// Current episode number.
|
|
/// </returns>
|
|
public int GetStepCount()
|
|
{
|
|
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
|
|
if (float.IsNaN(reward))
|
|
{
|
|
throw new ArgumentException("NaN reward passed to 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
|
|
if (float.IsNaN(increment))
|
|
{
|
|
throw new ArgumentException("NaN reward passed to 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();
|
|
_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 ActionMasker(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)
|
|
{
|
|
if (param.vectorActionSpaceType == SpaceType.Continuous)
|
|
{
|
|
m_Action.vectorActions = new float[param.vectorActionSize[0]];
|
|
m_Info.storedVectorActions = new float[param.vectorActionSize[0]];
|
|
}
|
|
else
|
|
{
|
|
m_Action.vectorActions = new float[param.vectorActionSize.Length];
|
|
m_Info.storedVectorActions = new float[param.vectorActionSize.Length];
|
|
}
|
|
}
|
|
}
|
|
|
|
/// <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()
|
|
{
|
|
throw new UnityAgentsException(string.Format(
|
|
"The Heuristic method was not implemented for the Agent on the " +
|
|
"{0} GameObject.",
|
|
gameObject.name));
|
|
}
|
|
|
|
/// <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_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 = false;
|
|
m_Info.maxStepReached = false;
|
|
m_Info.episodeId = m_EpisodeId;
|
|
|
|
m_Brain.RequestDecision(m_Info, sensors);
|
|
|
|
if (m_Recorder != null && m_Recorder.record && Application.isEditor)
|
|
{
|
|
m_Recorder.WriteExperience(m_Info, sensors);
|
|
}
|
|
}
|
|
|
|
void UpdateSensors()
|
|
{
|
|
for (var i = 0; i < sensors.Count; i++)
|
|
{
|
|
sensors[i].Update();
|
|
}
|
|
}
|
|
|
|
/// <summary>
|
|
/// Collects the (vector, visual) observations of the agent.
|
|
/// The agent observation describes the current environment from the
|
|
/// perspective of the agent.
|
|
/// </summary>
|
|
/// <remarks>
|
|
/// Simply, an agents observation is any environment information that helps
|
|
/// the Agent acheive 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:
|
|
/// - <see cref="AddVectorObs(int)"/>
|
|
/// - <see cref="AddVectorObs(float)"/>
|
|
/// - <see cref="AddVectorObs(Vector3)"/>
|
|
/// - <see cref="AddVectorObs(Vector2)"/>
|
|
/// - <see>
|
|
/// <cref>AddVectorObs(float[])</cref>
|
|
/// </see>
|
|
/// - <see>
|
|
/// <cref>AddVectorObs(List{float})</cref>
|
|
/// </see>
|
|
/// - <see cref="AddVectorObs(Quaternion)"/>
|
|
/// - <see cref="AddVectorObs(bool)"/>
|
|
/// - <see cref="AddVectorObs(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()
|
|
{
|
|
}
|
|
|
|
/// <summary>
|
|
/// Sets an action mask for discrete control agents. When used, the agent will not be
|
|
/// able to perform the action passed as argument at the next decision. If no branch is
|
|
/// specified, the default branch will be 0. The actionIndex or actionIndices correspond
|
|
/// to the action the agent will be unable to perform.
|
|
/// </summary>
|
|
/// <param name="actionIndices">The indices of the masked actions on branch 0</param>
|
|
protected void SetActionMask(IEnumerable<int> actionIndices)
|
|
{
|
|
m_ActionMasker.SetActionMask(0, actionIndices);
|
|
}
|
|
|
|
/// <summary>
|
|
/// Sets an action mask for discrete control agents. When used, the agent will not be
|
|
/// able to perform the action passed as argument at the next decision. If no branch is
|
|
/// specified, the default branch will be 0. The actionIndex or actionIndices correspond
|
|
/// to the action the agent will be unable to perform.
|
|
/// </summary>
|
|
/// <param name="actionIndex">The index of the masked action on branch 0</param>
|
|
protected void SetActionMask(int actionIndex)
|
|
{
|
|
m_ActionMasker.SetActionMask(0, new[] { actionIndex });
|
|
}
|
|
|
|
/// <summary>
|
|
/// Sets an action mask for discrete control agents. When used, the agent will not be
|
|
/// able to perform the action passed as argument at the next decision. If no branch is
|
|
/// specified, the default branch will be 0. The actionIndex or actionIndices correspond
|
|
/// to the action the agent will be unable to perform.
|
|
/// </summary>
|
|
/// <param name="branch">The branch for which the actions will be masked</param>
|
|
/// <param name="actionIndex">The index of the masked action</param>
|
|
protected void SetActionMask(int branch, int actionIndex)
|
|
{
|
|
m_ActionMasker.SetActionMask(branch, new[] { actionIndex });
|
|
}
|
|
|
|
/// <summary>
|
|
/// Modifies an action mask for discrete control agents. When used, the agent will not be
|
|
/// able to perform the action passed as argument at the next decision. If no branch is
|
|
/// specified, the default branch will be 0. The actionIndex or actionIndices correspond
|
|
/// to the action the agent will be unable to perform.
|
|
/// </summary>
|
|
/// <param name="branch">The branch for which the actions will be masked</param>
|
|
/// <param name="actionIndices">The indices of the masked actions</param>
|
|
protected void SetActionMask(int branch, IEnumerable<int> actionIndices)
|
|
{
|
|
m_ActionMasker.SetActionMask(branch, actionIndices);
|
|
}
|
|
|
|
/// <summary>
|
|
/// Adds a float observation to the vector observations of the agent.
|
|
/// Increases the size of the agents vector observation by 1.
|
|
/// </summary>
|
|
/// <param name="observation">Observation.</param>
|
|
protected void AddVectorObs(float observation)
|
|
{
|
|
collectObservationsSensor.AddObservation(observation);
|
|
}
|
|
|
|
/// <summary>
|
|
/// Adds an integer observation to the vector observations of the agent.
|
|
/// Increases the size of the agents vector observation by 1.
|
|
/// </summary>
|
|
/// <param name="observation">Observation.</param>
|
|
protected void AddVectorObs(int observation)
|
|
{
|
|
collectObservationsSensor.AddObservation(observation);
|
|
}
|
|
|
|
/// <summary>
|
|
/// Adds an Vector3 observation to the vector observations of the agent.
|
|
/// Increases the size of the agents vector observation by 3.
|
|
/// </summary>
|
|
/// <param name="observation">Observation.</param>
|
|
protected void AddVectorObs(Vector3 observation)
|
|
{
|
|
collectObservationsSensor.AddObservation(observation);
|
|
}
|
|
|
|
/// <summary>
|
|
/// Adds an Vector2 observation to the vector observations of the agent.
|
|
/// Increases the size of the agents vector observation by 2.
|
|
/// </summary>
|
|
/// <param name="observation">Observation.</param>
|
|
protected void AddVectorObs(Vector2 observation)
|
|
{
|
|
collectObservationsSensor.AddObservation(observation);
|
|
}
|
|
|
|
/// <summary>
|
|
/// Adds a collection of float observations to the vector observations of the agent.
|
|
/// Increases the size of the agents vector observation by size of the collection.
|
|
/// </summary>
|
|
/// <param name="observation">Observation.</param>
|
|
protected void AddVectorObs(IEnumerable<float> observation)
|
|
{
|
|
collectObservationsSensor.AddObservation(observation);
|
|
}
|
|
|
|
/// <summary>
|
|
/// Adds a quaternion observation to the vector observations of the agent.
|
|
/// Increases the size of the agents vector observation by 4.
|
|
/// </summary>
|
|
/// <param name="observation">Observation.</param>
|
|
protected void AddVectorObs(Quaternion observation)
|
|
{
|
|
collectObservationsSensor.AddObservation(observation);
|
|
}
|
|
|
|
/// <summary>
|
|
/// Adds a boolean observation to the vector observation of the agent.
|
|
/// Increases the size of the agent's vector observation by 1.
|
|
/// </summary>
|
|
/// <param name="observation"></param>
|
|
protected void AddVectorObs(bool observation)
|
|
{
|
|
collectObservationsSensor.AddObservation(observation);
|
|
}
|
|
|
|
protected void AddVectorObs(int observation, int range)
|
|
{
|
|
collectObservationsSensor.AddOneHotObservation(observation, range);
|
|
}
|
|
|
|
/// <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 (returns null if no decision has been made)
|
|
/// </summary>
|
|
public float[] GetAction()
|
|
{
|
|
return m_Action.vectorActions;
|
|
}
|
|
|
|
/// <summary>
|
|
/// This method will forcefully reset the agent and will also reset the hasAlreadyReset flag.
|
|
/// This way, even if the agent was already in the process of reseting, it will be reset again
|
|
/// and will not send a Done flag at the next step.
|
|
/// </summary>
|
|
void ForceReset()
|
|
{
|
|
_AgentReset();
|
|
}
|
|
|
|
/// <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 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;
|
|
}
|
|
}
|
|
|
|
/// Used by the brain to make the agent perform a step.
|
|
void AgentStep()
|
|
{
|
|
if ((m_StepCount >= maxStep - 1) && (maxStep > 0))
|
|
{
|
|
NotifyAgentDone(true);
|
|
_AgentReset();
|
|
}
|
|
else
|
|
{
|
|
m_StepCount += 1;
|
|
}
|
|
if ((m_RequestAction) && (m_Brain != null))
|
|
{
|
|
m_RequestAction = false;
|
|
if (m_Action.vectorActions != null)
|
|
{
|
|
AgentAction(m_Action.vectorActions);
|
|
}
|
|
}
|
|
}
|
|
|
|
void DecideAction()
|
|
{
|
|
m_Action.vectorActions = m_Brain?.DecideAction();
|
|
}
|
|
}
|
|
}
|