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745 行
29 KiB
745 行
29 KiB
using System;
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using System.Collections.Generic;
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using UnityEngine;
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using Barracuda;
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namespace MLAgents
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{
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/// <summary>
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/// Struct that contains all the information for an Agent, including its
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/// observations, actions and current status.
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/// </summary>
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internal struct AgentInfo
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{
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/// <summary>
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/// Keeps track of the last vector action taken by the Brain.
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/// </summary>
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public float[] storedVectorActions;
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/// <summary>
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/// For discrete control, specifies the actions that the agent cannot take. Is true if
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/// the action is masked.
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/// </summary>
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public bool[] actionMasks;
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/// <summary>
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/// Current agent reward.
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/// </summary>
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public float reward;
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/// <summary>
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/// Whether the agent is done or not.
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/// </summary>
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public bool done;
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/// <summary>
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/// Whether the agent has reached its max step count for this episode.
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/// </summary>
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public bool maxStepReached;
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/// <summary>
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/// Episode identifier each agent receives at every reset. It is used
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/// to separate between different agents in the environment.
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/// </summary>
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public int episodeId;
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}
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/// <summary>
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/// Struct that contains the action information sent from the Brain to the
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/// Agent.
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/// </summary>
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internal struct AgentAction
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{
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public float[] vectorActions;
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}
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/// <summary>
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/// Agent MonoBehaviour class that is attached to a Unity GameObject, making it
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/// an Agent. An agent produces observations and takes actions in the
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/// environment. Observations are determined by the cameras attached
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/// to the agent in addition to the vector observations implemented by the
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/// user in <see cref="Agent.CollectObservations(VectorSensor)"/> or
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/// <see cref="Agent.CollectObservations(VectorSensor, ActionMasker)"/>.
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/// On the other hand, actions are determined by decisions produced by a Policy.
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/// Currently, this class is expected to be extended to implement the desired agent behavior.
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/// </summary>
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/// <remarks>
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/// Simply speaking, an agent roams through an environment and at each step
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/// of the environment extracts its current observation, sends them to its
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/// policy and in return receives an action. In practice,
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/// however, an agent need not send its observation at every step since very
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/// little may have changed between successive steps.
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///
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/// At any step, an agent may be considered done due to a variety of reasons:
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/// - The agent reached an end state within its environment.
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/// - The agent reached the maximum # of steps (i.e. timed out).
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/// - The academy reached the maximum # of steps (forced agent to be done).
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///
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/// Here, an agent reaches an end state if it completes its task successfully
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/// or somehow fails along the way. In the case where an agent is done before
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/// the academy, it either resets and restarts, or just lingers until the
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/// academy is done.
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///
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/// An important note regarding steps and episodes is due. Here, an agent step
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/// corresponds to an academy step, which also corresponds to Unity
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/// environment step (i.e. each FixedUpdate call). This is not the case for
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/// episodes. The academy controls the global episode count and each agent
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/// controls its own local episode count and can reset and start a new local
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/// episode independently (based on its own experience). Thus an academy
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/// (global) episode can be viewed as the upper-bound on an agents episode
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/// length and that within a single global episode, an agent may have completed
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/// multiple local episodes. Consequently, if an agent max step is
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/// set to a value larger than the academy max steps value, then the academy
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/// value takes precedence (since the agent max step will never be reached).
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///
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/// Lastly, note that at any step the policy to the agent is allowed to
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/// change model with <see cref="GiveModel"/>.
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///
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/// Implementation-wise, it is required that this class is extended and the
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/// virtual methods overridden. For sample implementations of agent behavior,
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/// see the Examples/ directory within this Unity project.
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/// </remarks>
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[HelpURL("https://github.com/Unity-Technologies/ml-agents/blob/master/" +
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"docs/Learning-Environment-Design-Agents.md")]
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[Serializable]
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[RequireComponent(typeof(BehaviorParameters))]
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public abstract class Agent : MonoBehaviour, ISerializationCallbackReceiver
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{
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IPolicy m_Brain;
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BehaviorParameters m_PolicyFactory;
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/// This code is here to make the upgrade path for users using maxStep
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/// easier. We will hook into the Serialization code and make sure that
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/// agentParameters.maxStep and this.maxStep are in sync.
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[Serializable]
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internal struct AgentParameters
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{
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public int maxStep;
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}
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[SerializeField][HideInInspector]
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internal AgentParameters agentParameters;
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[SerializeField][HideInInspector]
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internal bool hasUpgradedFromAgentParameters;
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/// <summary>
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/// The maximum number of steps the agent takes before being done.
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/// </summary>
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/// <remarks>
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/// If set to 0, the agent can only be set to done programmatically (or
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/// when the Academy is done).
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/// If set to any positive integer, the agent will be set to done after
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/// that many steps. Note that setting the max step to a value greater
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/// than the academy max step value renders it useless.
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/// </remarks>
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[HideInInspector] public int maxStep;
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/// Current Agent information (message sent to Brain).
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AgentInfo m_Info;
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/// Current Agent action (message sent from Brain).
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AgentAction m_Action;
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/// Represents the reward the agent accumulated during the current step.
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/// It is reset to 0 at the beginning of every step.
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/// Should be set to a positive value when the agent performs a "good"
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/// action that we wish to reinforce/reward, and set to a negative value
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/// when the agent performs a "bad" action that we wish to punish/deter.
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/// Additionally, the magnitude of the reward should not exceed 1.0
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float m_Reward;
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/// Keeps track of the cumulative reward in this episode.
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float m_CumulativeReward;
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/// Whether or not the agent requests an action.
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bool m_RequestAction;
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/// Whether or not the agent requests a decision.
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bool m_RequestDecision;
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/// Keeps track of the number of steps taken by the agent in this episode.
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/// Note that this value is different for each agent, and may not overlap
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/// with the step counter in the Academy, since agents reset based on
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/// their own experience.
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int m_StepCount;
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/// Episode identifier each agent receives. It is used
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/// to separate between different agents in the environment.
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/// This Id will be changed every time the Agent resets.
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int m_EpisodeId;
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/// Whether or not the Agent has been initialized already
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bool m_Initialized;
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/// Keeps track of the actions that are masked at each step.
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ActionMasker m_ActionMasker;
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/// <summary>
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/// Set of DemonstrationWriters that the Agent will write its step information to.
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/// If you use a DemonstrationRecorder component, this will automatically register its DemonstrationWriter.
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/// You can also add your own DemonstrationWriter by calling
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/// DemonstrationRecorder.AddDemonstrationWriterToAgent()
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/// </summary>
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internal ISet<DemonstrationWriter> DemonstrationWriters = new HashSet<DemonstrationWriter>();
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/// <summary>
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/// List of sensors used to generate observations.
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/// Currently generated from attached SensorComponents, and a legacy VectorSensor
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/// </summary>
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internal List<ISensor> sensors;
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/// <summary>
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/// VectorSensor which is written to by AddVectorObs
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/// </summary>
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internal VectorSensor collectObservationsSensor;
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void OnEnable()
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{
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LazyInitialize();
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}
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/// <summary>
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/// <inheritdoc cref="OnBeforeSerialize"/>
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/// </summary>
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public void OnBeforeSerialize()
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{
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// Manages a serialization upgrade issue from v0.13 to v0.14 where maxStep moved
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// from AgentParameters (since removed) to Agent
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if (maxStep == 0 && maxStep != agentParameters.maxStep && !hasUpgradedFromAgentParameters)
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{
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maxStep = agentParameters.maxStep;
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}
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hasUpgradedFromAgentParameters = true;
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}
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/// <summary>
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/// <inheritdoc cref="OnAfterDeserialize"/>
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/// </summary>
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public void OnAfterDeserialize()
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{
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// Manages a serialization upgrade issue from v0.13 to v0.14 where maxStep moved
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// from AgentParameters (since removed) to Agent
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if (maxStep == 0 && maxStep != agentParameters.maxStep && !hasUpgradedFromAgentParameters)
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{
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maxStep = agentParameters.maxStep;
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}
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hasUpgradedFromAgentParameters = true;
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}
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/// <summary>
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/// Initializes the agent. Can be safely called multiple times.
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/// </summary>
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public void LazyInitialize()
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{
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if (m_Initialized)
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{
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return;
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}
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m_Initialized = true;
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// Grab the "static" properties for the Agent.
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m_EpisodeId = EpisodeIdCounter.GetEpisodeId();
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m_PolicyFactory = GetComponent<BehaviorParameters>();
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m_Info = new AgentInfo();
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m_Action = new AgentAction();
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sensors = new List<ISensor>();
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Academy.Instance.AgentIncrementStep += AgentIncrementStep;
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Academy.Instance.AgentSendState += SendInfo;
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Academy.Instance.DecideAction += DecideAction;
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Academy.Instance.AgentAct += AgentStep;
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Academy.Instance.AgentForceReset += _AgentReset;
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m_Brain = m_PolicyFactory.GeneratePolicy(Heuristic);
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ResetData();
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InitializeAgent();
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InitializeSensors();
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}
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void OnDisable()
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{
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DemonstrationWriters.Clear();
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// If Academy.Dispose has already been called, we don't need to unregister with it.
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// We don't want to even try, because this will lazily create a new Academy!
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if (Academy.IsInitialized)
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{
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Academy.Instance.AgentIncrementStep -= AgentIncrementStep;
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Academy.Instance.AgentSendState -= SendInfo;
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Academy.Instance.DecideAction -= DecideAction;
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Academy.Instance.AgentAct -= AgentStep;
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Academy.Instance.AgentForceReset -= _AgentReset;
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}
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NotifyAgentDone();
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m_Brain?.Dispose();
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m_Initialized = false;
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}
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void NotifyAgentDone(bool maxStepReached = false)
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{
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m_Info.reward = m_Reward;
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m_Info.done = true;
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m_Info.maxStepReached = maxStepReached;
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// Request the last decision with no callbacks
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// We request a decision so Python knows the Agent is done immediately
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m_Brain?.RequestDecision(m_Info, sensors);
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// We also have to write any to any DemonstationStores so that they get the "done" flag.
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foreach(var demoWriter in DemonstrationWriters)
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{
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demoWriter.Record(m_Info, sensors);
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}
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UpdateRewardStats();
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// The Agent is done, so we give it a new episode Id
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m_EpisodeId = EpisodeIdCounter.GetEpisodeId();
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m_Reward = 0f;
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m_CumulativeReward = 0f;
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m_RequestAction = false;
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m_RequestDecision = false;
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}
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/// <summary>
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/// Updates the Model for the agent. Any model currently assigned to the
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/// agent will be replaced with the provided one. If the arguments are
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/// identical to the current parameters of the agent, the model will
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/// remain unchanged.
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/// </summary>
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/// <param name="behaviorName"> The identifier of the behavior. This
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/// will categorize the agent when training.
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/// </param>
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/// <param name="model"> The model to use for inference.</param>
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/// <param name = "inferenceDevice"> Define on what device the model
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/// will be run.</param>
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public void GiveModel(
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string behaviorName,
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NNModel model,
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InferenceDevice inferenceDevice = InferenceDevice.CPU)
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{
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m_PolicyFactory.GiveModel(behaviorName, model, inferenceDevice);
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m_Brain?.Dispose();
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m_Brain = m_PolicyFactory.GeneratePolicy(Heuristic);
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}
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/// <summary>
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/// Returns the current step counter (within the current episode).
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/// </summary>
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/// <returns>
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/// Current step count.
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/// </returns>
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public int StepCount
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{
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get { return m_StepCount; }
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}
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/// <summary>
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/// Overrides the current step reward of the agent and updates the episode
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/// reward accordingly.
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/// </summary>
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/// <param name="reward">The new value of the reward.</param>
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public void SetReward(float reward)
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{
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#if DEBUG
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Utilities.DebugCheckNanAndInfinity(reward, nameof(reward), nameof(SetReward));
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#endif
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m_CumulativeReward += (reward - m_Reward);
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m_Reward = reward;
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}
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/// <summary>
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/// Increments the step and episode rewards by the provided value.
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/// </summary>
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/// <param name="increment">Incremental reward value.</param>
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public void AddReward(float increment)
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{
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#if DEBUG
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Utilities.DebugCheckNanAndInfinity(increment, nameof(increment), nameof(AddReward));
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#endif
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m_Reward += increment;
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m_CumulativeReward += increment;
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}
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/// <summary>
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/// Retrieves the episode reward for the Agent.
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/// </summary>
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/// <returns>The episode reward.</returns>
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public float GetCumulativeReward()
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{
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return m_CumulativeReward;
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}
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void UpdateRewardStats()
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{
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var gaugeName = $"{m_PolicyFactory.behaviorName}.CumulativeReward";
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TimerStack.Instance.SetGauge(gaugeName, GetCumulativeReward());
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}
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/// <summary>
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/// Sets the done flag to true.
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/// </summary>
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public void Done()
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{
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NotifyAgentDone();
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_AgentReset();
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}
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/// <summary>
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/// Is called when the agent must request the brain for a new decision.
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/// </summary>
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public void RequestDecision()
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{
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m_RequestDecision = true;
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RequestAction();
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}
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/// <summary>
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/// Is called then the agent must perform a new action.
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/// </summary>
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public void RequestAction()
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{
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m_RequestAction = true;
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}
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/// Helper function that resets all the data structures associated with
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/// the agent. Typically used when the agent is being initialized or reset
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/// at the end of an episode.
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void ResetData()
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{
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var param = m_PolicyFactory.brainParameters;
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m_ActionMasker = new ActionMasker(param);
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// If we haven't initialized vectorActions, initialize to 0. This should only
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// happen during the creation of the Agent. In subsequent episodes, vectorAction
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// should stay the previous action before the Done(), so that it is properly recorded.
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if (m_Action.vectorActions == null)
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{
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if (param.vectorActionSpaceType == SpaceType.Continuous)
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{
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m_Action.vectorActions = new float[param.vectorActionSize[0]];
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m_Info.storedVectorActions = new float[param.vectorActionSize[0]];
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}
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else
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{
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m_Action.vectorActions = new float[param.vectorActionSize.Length];
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m_Info.storedVectorActions = new float[param.vectorActionSize.Length];
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}
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}
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}
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/// <summary>
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/// Initializes the agent, called once when the agent is enabled. Can be
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/// left empty if there is no special, unique set-up behavior for the
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/// agent.
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/// </summary>
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/// <remarks>
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/// One sample use is to store local references to other objects in the
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/// scene which would facilitate computing this agents observation.
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/// </remarks>
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public virtual void InitializeAgent()
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{
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}
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/// <summary>
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/// When the Agent uses Heuristics, it will call this method every time it
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/// needs an action. This can be used for debugging or controlling the agent
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/// with keyboard.
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/// </summary>
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/// <returns> A float array corresponding to the next action of the Agent
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/// </returns>
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public virtual float[] Heuristic()
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{
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throw new UnityAgentsException(
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"The Heuristic method was not implemented for the Agent on the " +
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$"{gameObject.name} GameObject.");
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}
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/// <summary>
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/// Set up the list of ISensors on the Agent. By default, this will select any
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/// SensorBase's attached to the Agent.
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/// </summary>
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internal void InitializeSensors()
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{
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// Get all attached sensor components
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SensorComponent[] attachedSensorComponents;
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if (m_PolicyFactory.useChildSensors)
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{
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attachedSensorComponents = GetComponentsInChildren<SensorComponent>();
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}
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else
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{
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attachedSensorComponents = GetComponents<SensorComponent>();
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}
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sensors.Capacity += attachedSensorComponents.Length;
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foreach (var component in attachedSensorComponents)
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{
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sensors.Add(component.CreateSensor());
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}
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// Support legacy CollectObservations
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var param = m_PolicyFactory.brainParameters;
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if (param.vectorObservationSize > 0)
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{
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collectObservationsSensor = new VectorSensor(param.vectorObservationSize);
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if (param.numStackedVectorObservations > 1)
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{
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var stackingSensor = new StackingSensor(
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collectObservationsSensor, param.numStackedVectorObservations);
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sensors.Add(stackingSensor);
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}
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else
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{
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sensors.Add(collectObservationsSensor);
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}
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}
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// Sort the Sensors by name to ensure determinism
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sensors.Sort((x, y) => x.GetName().CompareTo(y.GetName()));
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#if DEBUG
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// Make sure the names are actually unique
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for (var i = 0; i < sensors.Count - 1; i++)
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{
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Debug.Assert(
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!sensors[i].GetName().Equals(sensors[i + 1].GetName()),
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"Sensor names must be unique.");
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}
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#endif
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}
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/// <summary>
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/// Sends the Agent info to the linked Brain.
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/// </summary>
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void SendInfoToBrain()
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{
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if (m_Brain == null)
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{
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return;
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}
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m_Info.storedVectorActions = m_Action.vectorActions;
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m_ActionMasker.ResetMask();
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UpdateSensors();
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using (TimerStack.Instance.Scoped("CollectObservations"))
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{
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CollectObservations(collectObservationsSensor, m_ActionMasker);
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}
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m_Info.actionMasks = m_ActionMasker.GetMask();
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m_Info.reward = m_Reward;
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m_Info.done = false;
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m_Info.maxStepReached = false;
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m_Info.episodeId = m_EpisodeId;
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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 vector observations of the agent alongside the masked actions.
|
|
/// The agent observation describes the current environment from the
|
|
/// perspective of the agent.
|
|
/// </summary>
|
|
/// <param name="sensor">
|
|
/// The vector observations for the agent.
|
|
/// </param>
|
|
/// <param name="actionMasker">
|
|
/// The masked actions 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.
|
|
/// When using Discrete Control, you can prevent the Agent from using a certain
|
|
/// action by masking it. You can call the following method on the ActionMasker
|
|
/// input :
|
|
/// - <see cref="ActionMasker.SetActionMask(int)"/>
|
|
/// - <see cref="ActionMasker.SetActionMask(int, int)"/>
|
|
/// - <see cref="ActionMasker.SetActionMask(int, IEnumerable{int})"/>
|
|
/// - <see cref="ActionMasker.SetActionMask(IEnumerable{int})"/>
|
|
/// The branch input is the index of the action, actionIndices are the indices of the
|
|
/// invalid options for that action.
|
|
/// </remarks>
|
|
public virtual void CollectObservations(VectorSensor sensor, ActionMasker actionMasker)
|
|
{
|
|
CollectObservations(sensor);
|
|
}
|
|
|
|
/// <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>
|
|
/// 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 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(true);
|
|
_AgentReset();
|
|
}
|
|
}
|
|
|
|
void DecideAction()
|
|
{
|
|
m_Action.vectorActions = m_Brain?.DecideAction();
|
|
if (m_Action.vectorActions == null){
|
|
ResetData();
|
|
}
|
|
}
|
|
}
|
|
}
|