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1183 行
52 KiB
1183 行
52 KiB
using System;
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using System.Collections.Generic;
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using System.Collections.ObjectModel;
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using UnityEngine;
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using Unity.Barracuda;
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using MLAgents.Sensors;
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using MLAgents.Demonstrations;
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using MLAgents.Policies;
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using UnityEngine.Serialization;
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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.
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/// An element of the mask array is <c>true</c> if the action is prohibited.
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/// </summary>
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public bool[] discreteActionMasks;
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/// <summary>
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/// The 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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/// An agent is an actor that can observe its environment, decide on the
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/// best course of action using those observations, and execute those actions
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/// within the environment.
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/// </summary>
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/// <remarks>
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/// Use the Agent class as the subclass for implementing your own agents. Add
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/// your Agent implementation to a [GameObject] in the [Unity scene] that serves
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/// as the agent's environment.
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///
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/// Agents in an environment operate in *steps*. At each step, an agent collects observations,
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/// passes them to its decision-making policy, and receives an action vector in response.
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///
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/// Agents make observations using <see cref="ISensor"/> implementations. The ML-Agents
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/// API provides implementations for visual observations (<see cref="CameraSensor"/>)
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/// raycast observations (<see cref="RayPerceptionSensor"/>), and arbitrary
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/// data observations (<see cref="VectorSensor"/>). You can add the
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/// <see cref="CameraSensorComponent"/> and <see cref="RayPerceptionSensorComponent2D"/> or
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/// <see cref="RayPerceptionSensorComponent3D"/> components to an agent's [GameObject] to use
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/// those sensor types. You can implement the <see cref="CollectObservations(VectorSensor)"/>
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/// function in your Agent subclass to use a vector observation. The Agent class calls this
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/// function before it uses the observation vector to make a decision. (If you only use
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/// visual or raycast observations, you do not need to implement
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/// <see cref="CollectObservations"/>.)
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///
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/// Assign a decision making policy to an agent using a <see cref="BehaviorParameters"/>
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/// component attached to the agent's [GameObject]. The <see cref="BehaviorType"/> setting
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/// determines how decisions are made:
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///
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/// * <see cref="BehaviorType.Default"/>: decisions are made by the external process,
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/// when connected. Otherwise, decisions are made using inference. If no inference model
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/// is specified in the BehaviorParameters component, then heuristic decision
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/// making is used.
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/// * <see cref="BehaviorType.InferenceOnly"/>: decisions are always made using the trained
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/// model specified in the <see cref="BehaviorParameters"/> component.
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/// * <see cref="BehaviorType.HeuristicOnly"/>: when a decision is needed, the agent's
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/// <see cref="Heuristic"/> function is called. Your implementation is responsible for
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/// providing the appropriate action.
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///
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/// To trigger an agent decision automatically, you can attach a <see cref="DecisionRequester"/>
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/// component to the Agent game object. You can also call the agent's <see cref="RequestDecision"/>
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/// function manually. You only need to call <see cref="RequestDecision"/> when the agent is
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/// in a position to act upon the decision. In many cases, this will be every [FixedUpdate]
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/// callback, but could be less frequent. For example, an agent that hops around its environment
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/// can only take an action when it touches the ground, so several frames might elapse between
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/// one decision and the need for the next.
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///
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/// Use the <see cref="OnActionReceived"/> function to implement the actions your agent can take,
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/// such as moving to reach a goal or interacting with its environment.
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///
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/// When you call <see cref="EndEpisode"/> on an agent or the agent reaches its <see cref="maxStep"/> count,
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/// its current episode ends. You can reset the agent -- or remove it from the
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/// environment -- by implementing the <see cref="OnEpisodeBegin"/> function. An agent also
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/// becomes done when the <see cref="Academy"/> resets the environment, which only happens when
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/// the <see cref="Academy"/> receives a reset signal from an external process via the
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/// <see cref="Academy.Communicator"/>.
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///
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/// The Agent class extends the Unity [MonoBehaviour] class. You can implement the
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/// standard [MonoBehaviour] functions as needed for your agent. Since an agent's
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/// observations and actions typically take place during the [FixedUpdate] phase, you should
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/// only use the [MonoBehaviour.Update] function for cosmetic purposes. If you override the [MonoBehaviour]
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/// methods, [OnEnable()] or [OnDisable()], always call the base Agent class implementations.
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///
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/// You can implement the <see cref="Heuristic"/> function to specify agent actions using
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/// your own heuristic algorithm. Implementing a heuristic function can be useful
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/// for debugging. For example, you can use keyboard input to select agent actions in
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/// order to manually control an agent's behavior.
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///
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/// Note that you can change the inference model assigned to an agent at any step
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/// by calling <see cref="SetModel"/>.
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///
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/// See [Agents] and [Reinforcement Learning in Unity] in the [Unity ML-Agents Toolkit manual] for
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/// more information on creating and training agents.
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///
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/// For sample implementations of agent behavior, see the examples available in the
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/// [Unity ML-Agents Toolkit] on Github.
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///
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/// [MonoBehaviour]: https://docs.unity3d.com/ScriptReference/MonoBehaviour.html
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/// [GameObject]: https://docs.unity3d.com/Manual/GameObjects.html
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/// [Unity scene]: https://docs.unity3d.com/Manual/CreatingScenes.html
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/// [FixedUpdate]: https://docs.unity3d.com/ScriptReference/MonoBehaviour.FixedUpdate.html
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/// [MonoBehaviour.Update]: https://docs.unity3d.com/ScriptReference/MonoBehaviour.Update.html
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/// [OnEnable()]: https://docs.unity3d.com/ScriptReference/MonoBehaviour.OnEnable.html
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/// [OnDisable()]: https://docs.unity3d.com/ScriptReference/MonoBehaviour.OnDisable.html]
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/// [OnBeforeSerialize()]: https://docs.unity3d.com/ScriptReference/MonoBehaviour.OnBeforeSerialize.html
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/// [OnAfterSerialize()]: https://docs.unity3d.com/ScriptReference/MonoBehaviour.OnAfterSerialize.html
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/// [Agents]: https://github.com/Unity-Technologies/ml-agents/blob/0.15.1/docs/Learning-Environment-Design-Agents.md
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/// [Reinforcement Learning in Unity]: https://github.com/Unity-Technologies/ml-agents/blob/0.15.1/docs/Learning-Environment-Design.md
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/// [Unity ML-Agents Toolkit]: https://github.com/Unity-Technologies/ml-agents
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/// [Unity ML-Agents Toolkit manual]: https://github.com/Unity-Technologies/ml-agents/blob/0.15.1/docs/Readme.md
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///
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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 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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/// <value>The maximum steps for an agent to take before it resets; or 0 for
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/// unlimited steps.</value>
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/// <remarks>
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/// The max step value determines the maximum length of an agent's episodes.
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/// Set to a positive integer to limit the episode length to that many steps.
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/// Set to 0 for unlimited episode length.
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///
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/// When an episode ends and a new one begins, the Agent object's
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/// <seealso cref="OnEpisodeBegin"/> function is called. You can implement
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/// <see cref="OnEpisodeBegin"/> to reset the agent or remove it from the
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/// environment. An agent's episode can also end if you call its <seealso cref="EndEpisode"/>
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/// method or an external process resets the environment through the <see cref="Academy"/>.
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///
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/// Consider limiting the number of steps in an episode to avoid wasting time during
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/// training. If you set the max step value to a reasonable estimate of the time it should
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/// take to complete a task, then agents that haven’t succeeded in that time frame will
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/// reset and start a new training episode rather than continue to fail.
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/// </remarks>
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/// <example>
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/// To use a step limit when training while allowing agents to run without resetting
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/// outside of training, you can set the max step to 0 in <see cref="Initialize"/>
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/// if the <see cref="Academy"/> is not connected to an external process.
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/// <code>
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/// using MLAgents;
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///
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/// public class MyAgent : Agent
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/// {
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/// public override void Initialize()
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/// {
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/// if (!Academy.Instance.IsCommunicatorOn)
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/// {
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/// this.MaxStep = 0;
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/// }
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/// }
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/// }
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/// </code>
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/// **Note:** in general, you should limit the differences between the code you execute
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/// during training and the code you run during inference.
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/// </example>
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[FormerlySerializedAs("maxStep")]
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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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/// Number of times the Agent has completed an episode.
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int m_CompletedEpisodes;
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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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DiscreteActionMasker 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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/// <summary>
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/// Called when the attached <see cref="GameObject"/> becomes enabled and active.
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/// </summary>
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/// <remarks>
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/// This function initializes the Agent instance, if it hasn't been initialized yet.
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/// Always call the base Agent class version of this function if you implement `OnEnable()`
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/// in your own Agent subclasses.
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/// </remarks>
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/// <example>
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/// <code>
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/// protected override void OnEnable()
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/// {
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/// base.OnEnable();
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/// // additional OnEnable logic...
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/// }
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/// </code>
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/// </example>
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protected virtual void OnEnable()
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{
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LazyInitialize();
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}
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/// <summary>
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/// Called by Unity immediately before serializing this object.
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/// </summary>
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/// <remarks>
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/// The Agent class uses OnBeforeSerialize() for internal housekeeping. Call the
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/// base class implementation if you need your own custom serialization logic.
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///
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/// See [OnBeforeSerialize] for more information.
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///
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/// [OnBeforeSerialize]: https://docs.unity3d.com/ScriptReference/ISerializationCallbackReceiver.OnAfterDeserialize.html
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/// </remarks>
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/// <example>
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/// <code>
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/// public new void OnBeforeSerialize()
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/// {
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/// base.OnBeforeSerialize();
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/// // additional serialization logic...
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/// }
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/// </code>
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/// </example>
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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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/// Called by Unity immediately after deserializing this object.
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/// </summary>
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/// <remarks>
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/// The Agent class uses OnAfterDeserialize() for internal housekeeping. Call the
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/// base class implementation if you need your own custom deserialization logic.
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///
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/// See [OnAfterDeserialize] for more information.
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///
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/// [OnAfterDeserialize]: https://docs.unity3d.com/ScriptReference/ISerializationCallbackReceiver.OnAfterDeserialize.html
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/// </remarks>
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/// <example>
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/// <code>
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/// public new void OnAfterDeserialize()
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/// {
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/// base.OnAfterDeserialize();
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/// // additional deserialization logic...
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/// }
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/// </code>
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/// </example>
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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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/// <remarks>
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/// This function calls your <seealso cref="Initialize"/> implementation, if one exists.
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/// </remarks>
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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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Initialize();
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InitializeSensors();
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// The first time the Academy resets, all Agents in the scene will be
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// forced to reset through the <see cref="AgentForceReset"/> event.
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// To avoid the Agent resetting twice, the Agents will not begin their
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// episode when initializing until after the Academy had its first reset.
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if (Academy.Instance.TotalStepCount != 0)
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{
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OnEpisodeBegin();
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}
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}
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/// <summary>
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/// The reason that the Agent has been set to "done".
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/// </summary>
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enum DoneReason
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{
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/// <summary>
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/// The <see cref="EndEpisode"/> method was called.
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/// </summary>
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DoneCalled,
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/// <summary>
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/// The max steps for the Agent were reached.
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/// </summary>
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MaxStepReached,
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/// <summary>
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/// The Agent was disabled.
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/// </summary>
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Disabled,
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}
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/// <summary>
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/// Called when the attached <see cref="GameObject"/> becomes disabled and inactive.
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/// </summary>
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/// <remarks>
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/// Always call the base Agent class version of this function if you implement `OnDisable()`
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/// in your own Agent subclasses.
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/// </remarks>
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/// <example>
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/// <code>
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/// protected override void OnDisable()
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/// {
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/// base.OnDisable();
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/// // additional OnDisable logic...
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/// }
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/// </code>
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/// </example>
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/// <seealso cref="OnEnable"/>
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protected virtual 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(DoneReason.Disabled);
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m_Brain?.Dispose();
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m_Initialized = false;
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}
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void NotifyAgentDone(DoneReason doneReason)
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{
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if (m_Info.done)
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{
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// The Agent was already marked as Done and should not be notified again
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return;
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}
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m_Info.episodeId = m_EpisodeId;
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m_Info.reward = m_Reward;
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m_Info.done = true;
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m_Info.maxStepReached = doneReason == DoneReason.MaxStepReached;
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if (collectObservationsSensor != null)
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{
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// Make sure the latest observations are being passed to training.
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collectObservationsSensor.Reset();
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CollectObservations(collectObservationsSensor);
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}
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|
// Request the last decision with no callbacks
|
|
// We request a decision so Python knows the Agent is done immediately
|
|
m_Brain?.RequestDecision(m_Info, sensors);
|
|
ResetSensors();
|
|
|
|
// We also have to write any to any DemonstationStores so that they get the "done" flag.
|
|
foreach (var demoWriter in DemonstrationWriters)
|
|
{
|
|
demoWriter.Record(m_Info, sensors);
|
|
}
|
|
|
|
if (doneReason != DoneReason.Disabled)
|
|
{
|
|
// We don't want to update the reward stats when the Agent is disabled, because this will make
|
|
// the rewards look lower than they actually are during shutdown.
|
|
m_CompletedEpisodes++;
|
|
UpdateRewardStats();
|
|
}
|
|
|
|
m_Reward = 0f;
|
|
m_CumulativeReward = 0f;
|
|
m_RequestAction = false;
|
|
m_RequestDecision = false;
|
|
Array.Clear(m_Info.storedVectorActions, 0, m_Info.storedVectorActions.Length);
|
|
}
|
|
|
|
/// <summary>
|
|
/// Updates the Model assigned to this Agent instance.
|
|
/// </summary>
|
|
/// <remarks>
|
|
/// If the agent already has an assigned model, that model is replaced with the
|
|
/// the provided one. However, if you call this function with arguments that are
|
|
/// identical to the current parameters of the agent, then no changes are made.
|
|
///
|
|
/// **Note:** the <paramref name="behaviorName"/> parameter is ignored when not training.
|
|
/// The <paramref name="model"/> and <paramref name="inferenceDevice"/> parameters
|
|
/// are ignored when not using inference.
|
|
/// </remarks>
|
|
/// <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 the device on which the model
|
|
/// will be run.</param>
|
|
public void SetModel(
|
|
string behaviorName,
|
|
NNModel model,
|
|
InferenceDevice inferenceDevice = InferenceDevice.CPU)
|
|
{
|
|
if (behaviorName == m_PolicyFactory.BehaviorName &&
|
|
model == m_PolicyFactory.Model &&
|
|
inferenceDevice == m_PolicyFactory.InferenceDevice)
|
|
{
|
|
// If everything is the same, don't make any changes.
|
|
return;
|
|
}
|
|
NotifyAgentDone(DoneReason.Disabled);
|
|
m_PolicyFactory.Model = model;
|
|
m_PolicyFactory.InferenceDevice = inferenceDevice;
|
|
m_PolicyFactory.BehaviorName = behaviorName;
|
|
ReloadPolicy();
|
|
}
|
|
|
|
internal void ReloadPolicy()
|
|
{
|
|
if (!m_Initialized)
|
|
{
|
|
// If we haven't initialized yet, no need to make any changes now; they'll
|
|
// happen in LazyInitialize later.
|
|
return;
|
|
}
|
|
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>
|
|
/// Returns the number of episodes that the Agent has completed (either <see cref="Agent.EndEpisode()"/>
|
|
/// was called, or maxSteps was reached).
|
|
/// </summary>
|
|
/// <returns>
|
|
/// Current episode count.
|
|
/// </returns>
|
|
public int CompletedEpisodes
|
|
{
|
|
get { return m_CompletedEpisodes; }
|
|
}
|
|
|
|
/// <summary>
|
|
/// Overrides the current step reward of the agent and updates the episode
|
|
/// reward accordingly.
|
|
/// </summary>
|
|
/// <remarks>
|
|
/// This function replaces any rewards given to the agent during the current step.
|
|
/// Use <see cref="AddReward(float)"/> to incrementally change the reward rather than
|
|
/// overriding it.
|
|
///
|
|
/// Typically, you assign rewards in the Agent subclass's <see cref="OnActionReceived(float[])"/>
|
|
/// implementation after carrying out the received action and evaluating its success.
|
|
///
|
|
/// Rewards are used during reinforcement learning; they are ignored during inference.
|
|
///
|
|
/// See [Agents - Rewards] for general advice on implementing rewards and [Reward Signals]
|
|
/// for information about mixing reward signals from curiosity and Generative Adversarial
|
|
/// Imitation Learning (GAIL) with rewards supplied through this method.
|
|
///
|
|
/// [Agents - Rewards]: https://github.com/Unity-Technologies/ml-agents/blob/0.15.1/docs/Learning-Environment-Design-Agents.md#rewards
|
|
/// [Reward Signals]: https://github.com/Unity-Technologies/ml-agents/blob/0.15.1/docs/Reward-Signals.md
|
|
/// </remarks>
|
|
/// <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>
|
|
/// <remarks>Use a positive reward to reinforce desired behavior. You can use a
|
|
/// negative reward to penalize mistakes. Use <seealso cref="SetReward(float)"/> to
|
|
/// set the reward assigned to the current step with a specific value rather than
|
|
/// increasing or decreasing it.
|
|
///
|
|
/// Typically, you assign rewards in the Agent subclass's <see cref="OnActionReceived(float[])"/>
|
|
/// implementation after carrying out the received action and evaluating its success.
|
|
///
|
|
/// Rewards are used during reinforcement learning; they are ignored during inference.
|
|
///
|
|
/// See [Agents - Rewards] for general advice on implementing rewards and [Reward Signals]
|
|
/// for information about mixing reward signals from curiosity and Generative Adversarial
|
|
/// Imitation Learning (GAIL) with rewards supplied through this method.
|
|
///
|
|
/// [Agents - Rewards]: https://github.com/Unity-Technologies/ml-agents/blob/0.15.1/docs/Learning-Environment-Design-Agents.md#rewards
|
|
/// [Reward Signals]: https://github.com/Unity-Technologies/ml-agents/blob/0.15.1/docs/Reward-Signals.md
|
|
///</remarks>
|
|
/// <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 and resets the agent.
|
|
/// </summary>
|
|
/// <seealso cref="OnEpisodeBegin"/>
|
|
public void EndEpisode()
|
|
{
|
|
NotifyAgentDone(DoneReason.DoneCalled);
|
|
_AgentReset();
|
|
}
|
|
|
|
/// <summary>
|
|
/// Requests a new decision for this agent.
|
|
/// </summary>
|
|
/// <remarks>
|
|
/// Call `RequestDecision()` whenever an agent needs a decision. You often
|
|
/// want to request a decision every environment step. However, if an agent
|
|
/// cannot use the decision every step, then you can request a decision less
|
|
/// frequently.
|
|
///
|
|
/// You can add a <seealso cref="DecisionRequester"/> component to the agent's
|
|
/// [GameObject] to drive the agent's decision making. When you use this component,
|
|
/// do not call `RequestDecision()` separately.
|
|
///
|
|
/// Note that this function calls <seealso cref="RequestAction"/>; you do not need to
|
|
/// call both functions at the same time.
|
|
///
|
|
/// [GameObject]: https://docs.unity3d.com/Manual/GameObjects.html
|
|
/// </remarks>
|
|
public void RequestDecision()
|
|
{
|
|
m_RequestDecision = true;
|
|
RequestAction();
|
|
}
|
|
|
|
/// <summary>
|
|
/// Requests an action for this agent.
|
|
/// </summary>
|
|
/// <remarks>
|
|
/// Call `RequestAction()` to repeat the previous action returned by the agent's
|
|
/// most recent decision. A new decision is not requested. When you call this function,
|
|
/// the Agent instance invokes <seealso cref="OnActionReceived(float[])"/> with the
|
|
/// existing action vector.
|
|
///
|
|
/// You can use `RequestAction()` in situations where an agent must take an action
|
|
/// every update, but doesn't need to make a decision as often. For example, an
|
|
/// agent that moves through its environment might need to apply an action to keep
|
|
/// moving, but only needs to make a decision to change course or speed occasionally.
|
|
///
|
|
/// You can add a <seealso cref="DecisionRequester"/> component to the agent's
|
|
/// [GameObject] to drive the agent's decision making and action frequency. When you
|
|
/// use this component, do not call `RequestAction()` separately.
|
|
///
|
|
/// Note that <seealso cref="RequestDecision"/> calls `RequestAction()`; you do not need to
|
|
/// call both functions at the same time.
|
|
///
|
|
/// [GameObject]: https://docs.unity3d.com/Manual/GameObjects.html
|
|
/// </remarks>
|
|
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>
|
|
/// Implement `Initialize()` to perform one-time initialization or set up of the
|
|
/// Agent instance.
|
|
/// </summary>
|
|
/// <remarks>
|
|
/// `Initialize()` is called once when the agent is first enabled. If, for example,
|
|
/// the Agent object needs references to other [GameObjects] in the scene, you
|
|
/// can collect and store those references here.
|
|
///
|
|
/// Note that <seealso cref="OnEpisodeBegin"/> is called at the start of each of
|
|
/// the agent's "episodes". You can use that function for items that need to be reset
|
|
/// for each episode.
|
|
///
|
|
/// [GameObject]: https://docs.unity3d.com/Manual/GameObjects.html
|
|
/// </remarks>
|
|
public virtual void Initialize() {}
|
|
|
|
/// <summary>
|
|
/// Implement `Heuristic()` to choose an action for this agent using a custom heuristic.
|
|
/// </summary>
|
|
/// <remarks>
|
|
/// Implement this function to provide custom decision making logic or to support manual
|
|
/// control of an agent using keyboard, mouse, or game controller input.
|
|
///
|
|
/// Your heuristic implementation can use any decision making logic you specify. Assign decision
|
|
/// values to the float[] array, <paramref cref="actionsOut"/>, passed to your function as a parameter.
|
|
/// Add values to the array at the same indexes as they are used in your
|
|
/// <seealso cref="OnActionReceived(float[])"/> function, which receives this array and
|
|
/// implements the corresponding agent behavior. See [Actions] for more information
|
|
/// about agent actions.
|
|
///
|
|
/// An agent calls this `Heuristic()` function to make a decision when you set its behavior
|
|
/// type to <see cref="BehaviorType.HeuristicOnly"/>. The agent also calls this function if
|
|
/// you set its behavior type to <see cref="BehaviorType.Default"/> when the
|
|
/// <see cref="Academy"/> is not connected to an external training process and you do not
|
|
/// assign a trained model to the agent.
|
|
///
|
|
/// To perform imitation learning, implement manual control of the agent in the `Heuristic()`
|
|
/// function so that you can record the demonstrations required for the imitation learning
|
|
/// algorithms. (Attach a [Demonstration Recorder] component to the agent's [GameObject] to
|
|
/// record the demonstration session to a file.)
|
|
///
|
|
/// Even when you don’t plan to use heuristic decisions for an agent or imitation learning,
|
|
/// implementing a simple heuristic function can aid in debugging agent actions and interactions
|
|
/// with its environment.
|
|
///
|
|
/// [Demonstration Recorder]: https://github.com/Unity-Technologies/ml-agents/blob/0.15.1/docs/Training-Imitation-Learning.md#recording-demonstrations
|
|
/// [Actions]: https://github.com/Unity-Technologies/ml-agents/blob/0.15.1/docs/Learning-Environment-Design-Agents.md#actions
|
|
/// [GameObject]: https://docs.unity3d.com/Manual/GameObjects.html
|
|
/// </remarks>
|
|
/// <example>
|
|
/// The following example illustrates a `Heuristic()` function that provides WASD-style
|
|
/// keyboard control for an agent that can move in two dimensions as well as jump. See
|
|
/// [Input Manager] for more information about the built-in Unity input functions.
|
|
/// You can also use the [Input System package], which provides a more flexible and
|
|
/// configurable input system.
|
|
/// <code>
|
|
/// public override void Heuristic(float[] actionsOut)
|
|
/// {
|
|
/// actionsOut[0] = Input.GetAxis("Horizontal");
|
|
/// actionsOut[1] = Input.GetKey(KeyCode.Space) ? 1.0f : 0.0f;
|
|
/// actionsOut[2] = Input.GetAxis("Vertical");
|
|
/// }
|
|
/// </code>
|
|
/// [Input Manager]: https://docs.unity3d.com/Manual/class-InputManager.html
|
|
/// [Input System package]: https://docs.unity3d.com/Packages/com.unity.inputsystem@1.0/manual/index.html
|
|
/// </example>
|
|
/// <param name="actionsOut">Array for the output actions.</param>
|
|
/// <seealso cref="OnActionReceived(float[])"/>
|
|
public virtual void Heuristic(float[] actionsOut)
|
|
{
|
|
Debug.LogWarning("Heuristic method called but not implemented. Returning placeholder actions.");
|
|
Array.Clear(actionsOut, 0, actionsOut.Length);
|
|
}
|
|
|
|
/// <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;
|
|
}
|
|
|
|
if (m_Info.done)
|
|
{
|
|
Array.Clear(m_Info.storedVectorActions, 0, m_Info.storedVectorActions.Length);
|
|
}
|
|
else
|
|
{
|
|
Array.Copy(m_Action.vectorActions, m_Info.storedVectorActions, m_Action.vectorActions.Length);
|
|
}
|
|
m_ActionMasker.ResetMask();
|
|
UpdateSensors();
|
|
using (TimerStack.Instance.Scoped("CollectObservations"))
|
|
{
|
|
CollectObservations(collectObservationsSensor);
|
|
}
|
|
using (TimerStack.Instance.Scoped("CollectDiscreteActionMasks"))
|
|
{
|
|
if (m_PolicyFactory.BrainParameters.VectorActionSpaceType == SpaceType.Discrete)
|
|
{
|
|
CollectDiscreteActionMasks(m_ActionMasker);
|
|
}
|
|
}
|
|
m_Info.discreteActionMasks = m_ActionMasker.GetMask();
|
|
|
|
m_Info.reward = m_Reward;
|
|
m_Info.done = false;
|
|
m_Info.maxStepReached = false;
|
|
m_Info.episodeId = m_EpisodeId;
|
|
|
|
m_Brain.RequestDecision(m_Info, sensors);
|
|
|
|
// If we have any DemonstrationWriters, write the AgentInfo and sensors to them.
|
|
foreach (var demoWriter in DemonstrationWriters)
|
|
{
|
|
demoWriter.Record(m_Info, sensors);
|
|
}
|
|
}
|
|
|
|
void UpdateSensors()
|
|
{
|
|
foreach (var sensor in sensors)
|
|
{
|
|
sensor.Update();
|
|
}
|
|
}
|
|
|
|
void ResetSensors()
|
|
{
|
|
foreach (var sensor in sensors)
|
|
{
|
|
sensor.Reset();
|
|
}
|
|
}
|
|
|
|
/// <summary>
|
|
/// Implement `CollectObservations()` to collect the vector observations of
|
|
/// the agent for the step. 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 agent's 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.
|
|
///
|
|
/// You can use a combination of vector, visual, and raycast observations for an
|
|
/// agent. If you only use visual or raycast observations, you do not need to
|
|
/// implement a `CollectObservations()` function.
|
|
///
|
|
/// Add vector observations to the <paramref name="sensor"/> parameter passed to
|
|
/// this method by calling the <seealso cref="VectorSensor"/> helper methods:
|
|
/// - <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)"/>
|
|
///
|
|
/// You can use any combination of these helper functions to build the agent's
|
|
/// vector of observations. You must build the vector in the same order
|
|
/// each time `CollectObservations()` is called and the length of the vector
|
|
/// must always be the same. In addition, the length of the observation must
|
|
/// match the <see cref="BrainParameters.VectorObservationSize"/>
|
|
/// attribute of the linked Brain, which is set in the Editor on the
|
|
/// **Behavior Parameters** component attached to the agent's [GameObject].
|
|
///
|
|
/// For more information about observations, see [Observations and Sensors].
|
|
///
|
|
/// [GameObject]: https://docs.unity3d.com/Manual/GameObjects.html
|
|
/// [Observations and Sensors]: https://github.com/Unity-Technologies/ml-agents/blob/0.15.1/docs/Learning-Environment-Design-Agents.md#observations-and-sensors
|
|
/// </remarks>
|
|
public virtual void CollectObservations(VectorSensor sensor)
|
|
{
|
|
}
|
|
|
|
/// <summary>
|
|
/// Returns a read-only view of the observations that were generated in
|
|
/// <see cref="CollectObservations(VectorSensor)"/>. This is mainly useful inside of a
|
|
/// <see cref="Heuristic(float[])"/> method to avoid recomputing the observations.
|
|
/// </summary>
|
|
/// <returns>A read-only view of the observations list.</returns>
|
|
public ReadOnlyCollection<float> GetObservations()
|
|
{
|
|
return collectObservationsSensor.GetObservations();
|
|
}
|
|
|
|
/// <summary>
|
|
/// Implement `CollectDiscreteActionMasks()` to 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})"/>.
|
|
///
|
|
/// See [Agents - Actions] for more information on masking actions.
|
|
///
|
|
/// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Learning-Environment-Design-Agents.md#actions
|
|
/// </remarks>
|
|
/// <seealso cref="OnActionReceived(float[])"/>
|
|
public virtual void CollectDiscreteActionMasks(DiscreteActionMasker actionMasker)
|
|
{
|
|
}
|
|
|
|
/// <summary>
|
|
/// Implement `OnActionReceived()` to specify agent behavior at every step, based
|
|
/// on the provided action.
|
|
/// </summary>
|
|
/// <remarks>
|
|
/// An action is passed to this function in the form of an array vector. Your
|
|
/// implementation must use the array to direct the agent's behavior for the
|
|
/// current step.
|
|
///
|
|
/// You decide how many elements you need in the action array to control your
|
|
/// agent and what each element means. For example, if you want to apply a
|
|
/// force to move an agent around the environment, you can arbitrarily pick
|
|
/// three values in the action array to use as the force components. During
|
|
/// training, the agent's policy learns to set those particular elements of
|
|
/// the array to maximize the training rewards the agent receives. (Of course,
|
|
/// if you implement a <seealso cref="Heuristic"/> function, it must use the same
|
|
/// elements of the action array for the same purpose since there is no learning
|
|
/// involved.)
|
|
///
|
|
/// Actions for an agent can be either *Continuous* or *Discrete*. Specify which
|
|
/// type of action space an agent uses, along with the size of the action array,
|
|
/// in the <see cref="BrainParameters"/> of the agent's associated
|
|
/// <see cref="BehaviorParameters"/> component.
|
|
///
|
|
/// When an agent uses the continuous action space, the values in the action
|
|
/// array are floating point numbers. You should clamp the values to the range,
|
|
/// -1..1, to increase numerical stability during training.
|
|
///
|
|
/// When an agent uses the discrete action space, the values in the action array
|
|
/// are integers that each represent a specific, discrete action. For example,
|
|
/// you could define a set of discrete actions such as:
|
|
///
|
|
/// <code>
|
|
/// 0 = Do nothing
|
|
/// 1 = Move one space left
|
|
/// 2 = Move one space right
|
|
/// 3 = Move one space up
|
|
/// 4 = Move one space down
|
|
/// </code>
|
|
///
|
|
/// When making a decision, the agent picks one of the five actions and puts the
|
|
/// corresponding integer value in the action vector. For example, if the agent
|
|
/// decided to move left, the action vector parameter would contain an array with
|
|
/// a single element with the value 1.
|
|
///
|
|
/// You can define multiple sets, or branches, of discrete actions to allow an
|
|
/// agent to perform simultaneous, independent actions. For example, you could
|
|
/// use one branch for movement and another branch for throwing a ball left, right,
|
|
/// up, or down, to allow the agent to do both in the same step.
|
|
///
|
|
/// The action vector of a discrete action space contains one element for each
|
|
/// branch. The value of each element is the integer representing the chosen
|
|
/// action for that branch. The agent always chooses one action for each
|
|
/// branch.
|
|
///
|
|
/// When you use the discrete action space, you can prevent the training process
|
|
/// or the neural network model from choosing specific actions in a step by
|
|
/// implementing the <see cref="CollectDiscreteActionMasks(DiscreteActionMasker)"/>
|
|
/// function. For example, if your agent is next to a wall, you could mask out any
|
|
/// actions that would result in the agent trying to move into the wall.
|
|
///
|
|
/// For more information about implementing agent actions see [Agents - Actions].
|
|
///
|
|
/// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/0.15.1/docs/Learning-Environment-Design-Agents.md#actions
|
|
/// </remarks>
|
|
/// <param name="vectorAction">
|
|
/// An array containing the action vector. The length of the array is specified
|
|
/// by the <see cref="BrainParameters"/> of the agent's associated
|
|
/// <see cref="BehaviorParameters"/> component.
|
|
/// </param>
|
|
public virtual void OnActionReceived(float[] vectorAction) {}
|
|
|
|
/// <summary>
|
|
/// Implement `OnEpisodeBegin()` to set up an Agent instance at the beginning
|
|
/// of an episode.
|
|
/// </summary>
|
|
/// <seealso cref="Initialize"/>
|
|
/// <seealso cref="EndEpisode"/>
|
|
public virtual void OnEpisodeBegin() {}
|
|
|
|
/// <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>
|
|
/// <seealso cref="OnActionReceived(float[])"/>
|
|
public float[] GetAction()
|
|
{
|
|
return m_Action.vectorActions;
|
|
}
|
|
|
|
/// <summary>
|
|
/// An internal reset method that updates internal data structures in
|
|
/// addition to calling <see cref="OnEpisodeBegin"/>.
|
|
/// </summary>
|
|
void _AgentReset()
|
|
{
|
|
ResetData();
|
|
m_StepCount = 0;
|
|
OnEpisodeBegin();
|
|
}
|
|
|
|
/// <summary>
|
|
/// Scales continuous action from [-1, 1] to arbitrary range.
|
|
/// </summary>
|
|
/// <param name="rawAction">The input action value.</param>
|
|
/// <param name="min">The minimum output value.</param>
|
|
/// <param name="max">The maximum output value.</param>
|
|
/// <returns>The <paramref name="rawAction"/> scaled from [-1,1] to
|
|
/// [<paramref name="min"/>, <paramref name="max"/>].</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 send 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;
|
|
OnActionReceived(m_Action.vectorActions);
|
|
}
|
|
|
|
if ((m_StepCount >= MaxStep) && (MaxStep > 0))
|
|
{
|
|
NotifyAgentDone(DoneReason.MaxStepReached);
|
|
_AgentReset();
|
|
}
|
|
}
|
|
|
|
void DecideAction()
|
|
{
|
|
if (m_Action.vectorActions == null)
|
|
{
|
|
ResetData();
|
|
}
|
|
var action = m_Brain?.DecideAction();
|
|
|
|
if (action == null)
|
|
{
|
|
Array.Clear(m_Action.vectorActions, 0, m_Action.vectorActions.Length);
|
|
}
|
|
else
|
|
{
|
|
Array.Copy(action, m_Action.vectorActions, action.Length);
|
|
}
|
|
}
|
|
}
|
|
}
|