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1009 行
37 KiB
1009 行
37 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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using MLAgents.Sensor;
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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, that is sent to the Brain.
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/// </summary>
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public struct AgentInfo
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{
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/// <summary>
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/// Most recent observations.
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/// </summary>
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public List<Observation> observations;
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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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/// Unique identifier each agent receives at initialization. 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 id;
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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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public struct AgentAction
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{
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public float[] vectorActions;
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public float value;
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}
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/// <summary>
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/// Struct that contains all the Agent-specific parameters provided in the
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/// Editor. This excludes the Brain linked to the Agent since it can be
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/// modified programmatically.
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/// </summary>
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[Serializable]
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public class AgentParameters
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{
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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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public int maxStep;
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/// <summary>
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/// Determines the behaviour of the agent when done.
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/// </summary>
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/// <remarks>
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/// If true, the agent will reset when done and start a new episode.
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/// Otherwise, the agent will remain done and its behavior will be
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/// dictated by the AgentOnDone method.
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/// </remarks>
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public bool resetOnDone = true;
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/// <summary>
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/// Whether to enable On Demand Decisions or make a decision at
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/// every step.
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/// </summary>
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public bool onDemandDecision;
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/// <summary>
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/// Number of actions between decisions (used when On Demand Decisions
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/// is turned off).
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/// </summary>
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public int numberOfActionsBetweenDecisions;
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}
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/// <summary>
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/// Agent Monobehavior 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="CollectObservations"/>. On the other hand, actions
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/// are determined by decisions produced by a Policy. Currently, this
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/// 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 <see cref="m_Done"/>.
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/// This could occur 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
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{
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IPolicy m_Brain;
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BehaviorParameters m_PolicyFactory;
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/// <summary>
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/// Agent parameters specified within the Editor via AgentEditor.
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/// </summary>
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[HideInInspector] public AgentParameters agentParameters;
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/// Current Agent information (message sent to Brain).
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AgentInfo m_Info;
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public AgentInfo Info
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{
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get { return m_Info; }
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set { m_Info = value; }
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}
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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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/// Whether or not the agent has completed the episode. This may be due
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/// to either reaching a success or fail state, or reaching the maximum
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/// number of steps (i.e. timing out).
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bool m_Done;
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/// Whether or not the agent reached the maximum number of steps.
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bool m_MaxStepReached;
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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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/// Flag to signify that an agent has been reset but the fact that it is
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/// done has not been communicated (required for On Demand Decisions).
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bool m_HasAlreadyReset;
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/// Flag to signify that an agent is done and should not reset until
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/// the fact that it is done has been communicated.
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bool m_Terminate;
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/// Unique identifier each agent receives at initialization. It is used
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/// to separate between different agents in the environment.
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int m_Id;
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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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/// Demonstration recorder.
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/// </summary>
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DemonstrationRecorder m_Recorder;
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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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[FormerlySerializedAs("m_Sensors")]
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public 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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public VectorSensor collectObservationsSensor;
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/// <summary>
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/// Internal buffer used for generating float observations.
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/// </summary>
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float[] m_VectorSensorBuffer;
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WriteAdapter m_WriteAdapter = new WriteAdapter();
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/// MonoBehaviour function that is called when the attached GameObject
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/// becomes enabled or active.
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void OnEnable()
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{
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m_Id = gameObject.GetInstanceID();
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var academy = FindObjectOfType<Academy>();
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academy.LazyInitialization();
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OnEnableHelper(academy);
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m_Recorder = GetComponent<DemonstrationRecorder>();
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}
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/// Helper method for the <see cref="OnEnable"/> event, created to
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/// facilitate testing.
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void OnEnableHelper(Academy academy)
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{
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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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if (academy == null)
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{
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throw new UnityAgentsException(
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"No Academy Component could be found in the scene.");
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}
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academy.AgentSetStatus += SetStatus;
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academy.AgentResetIfDone += ResetIfDone;
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academy.AgentSendState += SendInfo;
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academy.DecideAction += DecideAction;
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academy.AgentAct += AgentStep;
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academy.AgentForceReset += _AgentReset;
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m_PolicyFactory = GetComponent<BehaviorParameters>();
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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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/// Monobehavior function that is called when the attached GameObject
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/// becomes disabled or inactive.
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void OnDisable()
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{
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var academy = FindObjectOfType<Academy>();
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if (academy != null)
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{
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academy.AgentSetStatus -= SetStatus;
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academy.AgentResetIfDone -= ResetIfDone;
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academy.AgentSendState -= SendInfo;
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academy.DecideAction -= DecideAction;
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academy.AgentAct -= AgentStep;
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academy.AgentForceReset -= ForceReset;
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}
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m_Brain?.Dispose();
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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 epside).
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/// </summary>
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/// <returns>
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/// Current episode number.
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/// </returns>
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public int GetStepCount()
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{
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return m_StepCount;
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}
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/// <summary>
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/// Resets the step reward and possibly the episode reward for the agent.
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/// </summary>
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public void ResetReward()
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{
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m_Reward = 0f;
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if (m_Done)
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{
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m_CumulativeReward = 0f;
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}
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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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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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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 step reward for the Agent.
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/// </summary>
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/// <returns>The step reward.</returns>
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public float GetReward()
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{
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return m_Reward;
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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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/// <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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m_Done = true;
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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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/// <summary>
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/// Indicates if the agent has reached his maximum number of steps.
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/// </summary>
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/// <returns>
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/// <c>true</c>, if max step reached was reached, <c>false</c> otherwise.
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/// </returns>
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public bool IsMaxStepReached()
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{
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return m_MaxStepReached;
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}
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/// <summary>
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/// Indicates if the agent is done
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/// </summary>
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/// <returns>
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/// <c>true</c>, if the agent is done, <c>false</c> otherwise.
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/// </returns>
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public bool IsDone()
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{
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return m_Done;
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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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m_Info.observations = new List<Observation>();
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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(string.Format(
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"The Heuristic method was not implemented for the Agent on the " +
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"{0} GameObject.",
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gameObject.name));
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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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public void InitializeSensors()
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{
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// Get all attached sensor components
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var attachedSensorComponents = GetComponents<SensorComponent>();
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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(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(!sensors[i].GetName().Equals(sensors[i + 1].GetName()), "Sensor names must be unique.");
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}
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#endif
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// Create a buffer for writing vector sensor data too
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int numFloatObservations = 0;
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for (var i = 0; i < sensors.Count; i++)
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{
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if (sensors[i].GetCompressionType() == SensorCompressionType.None)
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{
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numFloatObservations += sensors[i].ObservationSize();
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}
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}
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m_VectorSensorBuffer = new float[numFloatObservations];
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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>
|
|
void SendInfoToBrain()
|
|
{
|
|
if (m_Brain == null)
|
|
{
|
|
return;
|
|
}
|
|
|
|
m_Info.storedVectorActions = m_Action.vectorActions;
|
|
m_Info.observations.Clear();
|
|
m_ActionMasker.ResetMask();
|
|
UpdateSensors();
|
|
using (TimerStack.Instance.Scoped("CollectObservations"))
|
|
{
|
|
CollectObservations();
|
|
}
|
|
m_Info.actionMasks = m_ActionMasker.GetMask();
|
|
|
|
// var param = m_PolicyFactory.brainParameters; // look, no brain params!
|
|
|
|
m_Info.reward = m_Reward;
|
|
m_Info.done = m_Done;
|
|
m_Info.maxStepReached = m_MaxStepReached;
|
|
m_Info.id = m_Id;
|
|
|
|
m_Brain.RequestDecision(this);
|
|
|
|
if (m_Recorder != null && m_Recorder.record && Application.isEditor)
|
|
{
|
|
// This is a bit of a hack - if we're in inference mode, observations won't be generated
|
|
// But we need these to be generated for the recorder. So generate them here.
|
|
if (m_Info.observations.Count == 0)
|
|
{
|
|
GenerateSensorData();
|
|
}
|
|
|
|
m_Recorder.WriteExperience(m_Info);
|
|
}
|
|
|
|
}
|
|
|
|
void UpdateSensors()
|
|
{
|
|
for (var i = 0; i < sensors.Count; i++)
|
|
{
|
|
sensors[i].Update();
|
|
}
|
|
}
|
|
|
|
/// <summary>
|
|
/// Generate data for each sensor and store it on the Agent's AgentInfo.
|
|
/// NOTE: At the moment, this is only called during training or when using a DemonstrationRecorder;
|
|
/// during inference the Sensors are used to write directly to the Tensor data. This will likely change in the
|
|
/// future to be controlled by the type of brain being used.
|
|
/// </summary>
|
|
public void GenerateSensorData()
|
|
{
|
|
int floatsWritten = 0;
|
|
// Generate data for all Sensors
|
|
for (var i = 0; i < sensors.Count; i++)
|
|
{
|
|
var sensor = sensors[i];
|
|
if (sensor.GetCompressionType() == SensorCompressionType.None)
|
|
{
|
|
// only handles 1D
|
|
// TODO handle in communicator code instead
|
|
m_WriteAdapter.SetTarget(m_VectorSensorBuffer, floatsWritten);
|
|
var numFloats = sensor.Write(m_WriteAdapter);
|
|
var floatObs = new Observation
|
|
{
|
|
FloatData = new ArraySegment<float>(m_VectorSensorBuffer, floatsWritten, numFloats),
|
|
Shape = sensor.GetFloatObservationShape(),
|
|
CompressionType = sensor.GetCompressionType()
|
|
};
|
|
m_Info.observations.Add(floatObs);
|
|
floatsWritten += numFloats;
|
|
}
|
|
else
|
|
{
|
|
var compressedObs = new Observation
|
|
{
|
|
CompressedData = sensor.GetCompressedObservation(),
|
|
Shape = sensor.GetFloatObservationShape(),
|
|
CompressionType = sensor.GetCompressionType()
|
|
};
|
|
m_Info.observations.Add(compressedObs);
|
|
}
|
|
}
|
|
}
|
|
|
|
/// <summary>
|
|
/// Collects the (vector, visual) observations of the agent.
|
|
/// The agent observation describes the current environment from the
|
|
/// perspective of the agent.
|
|
/// </summary>
|
|
/// <remarks>
|
|
/// Simply, an agents observation is any environment information that helps
|
|
/// the Agent acheive its goal. For example, for a fighting Agent, its
|
|
/// observation could include distances to friends or enemies, or the
|
|
/// current level of ammunition at its disposal.
|
|
/// Recall that an Agent may attach vector or visual observations.
|
|
/// Vector observations are added by calling the provided helper methods:
|
|
/// - <see cref="AddVectorObs(int)"/>
|
|
/// - <see cref="AddVectorObs(float)"/>
|
|
/// - <see cref="AddVectorObs(Vector3)"/>
|
|
/// - <see cref="AddVectorObs(Vector2)"/>
|
|
/// - <see>
|
|
/// <cref>AddVectorObs(float[])</cref>
|
|
/// </see>
|
|
/// - <see>
|
|
/// <cref>AddVectorObs(List{float})</cref>
|
|
/// </see>
|
|
/// - <see cref="AddVectorObs(Quaternion)"/>
|
|
/// - <see cref="AddVectorObs(bool)"/>
|
|
/// - <see cref="AddVectorObs(int, int)"/>
|
|
/// Depending on your environment, any combination of these helpers can
|
|
/// be used. They just need to be used in the exact same order each time
|
|
/// this method is called and the resulting size of the vector observation
|
|
/// needs to match the vectorObservationSize attribute of the linked Brain.
|
|
/// Visual observations are implicitly added from the cameras attached to
|
|
/// the Agent.
|
|
/// </remarks>
|
|
public virtual void CollectObservations()
|
|
{
|
|
}
|
|
|
|
/// <summary>
|
|
/// Sets an action mask for discrete control agents. When used, the agent will not be
|
|
/// able to perform the action passed as argument at the next decision. If no branch is
|
|
/// specified, the default branch will be 0. The actionIndex or actionIndices correspond
|
|
/// to the action the agent will be unable to perform.
|
|
/// </summary>
|
|
/// <param name="actionIndices">The indices of the masked actions on branch 0</param>
|
|
protected void SetActionMask(IEnumerable<int> actionIndices)
|
|
{
|
|
m_ActionMasker.SetActionMask(0, actionIndices);
|
|
}
|
|
|
|
/// <summary>
|
|
/// Sets an action mask for discrete control agents. When used, the agent will not be
|
|
/// able to perform the action passed as argument at the next decision. If no branch is
|
|
/// specified, the default branch will be 0. The actionIndex or actionIndices correspond
|
|
/// to the action the agent will be unable to perform.
|
|
/// </summary>
|
|
/// <param name="actionIndex">The index of the masked action on branch 0</param>
|
|
protected void SetActionMask(int actionIndex)
|
|
{
|
|
m_ActionMasker.SetActionMask(0, new[] { actionIndex });
|
|
}
|
|
|
|
/// <summary>
|
|
/// Sets an action mask for discrete control agents. When used, the agent will not be
|
|
/// able to perform the action passed as argument at the next decision. If no branch is
|
|
/// specified, the default branch will be 0. The actionIndex or actionIndices correspond
|
|
/// to the action the agent will be unable to perform.
|
|
/// </summary>
|
|
/// <param name="branch">The branch for which the actions will be masked</param>
|
|
/// <param name="actionIndex">The index of the masked action</param>
|
|
protected void SetActionMask(int branch, int actionIndex)
|
|
{
|
|
m_ActionMasker.SetActionMask(branch, new[] { actionIndex });
|
|
}
|
|
|
|
/// <summary>
|
|
/// Modifies an action mask for discrete control agents. When used, the agent will not be
|
|
/// able to perform the action passed as argument at the next decision. If no branch is
|
|
/// specified, the default branch will be 0. The actionIndex or actionIndices correspond
|
|
/// to the action the agent will be unable to perform.
|
|
/// </summary>
|
|
/// <param name="branch">The branch for which the actions will be masked</param>
|
|
/// <param name="actionIndices">The indices of the masked actions</param>
|
|
protected void SetActionMask(int branch, IEnumerable<int> actionIndices)
|
|
{
|
|
m_ActionMasker.SetActionMask(branch, actionIndices);
|
|
}
|
|
|
|
/// <summary>
|
|
/// Adds a float observation to the vector observations of the agent.
|
|
/// Increases the size of the agents vector observation by 1.
|
|
/// </summary>
|
|
/// <param name="observation">Observation.</param>
|
|
protected void AddVectorObs(float observation)
|
|
{
|
|
collectObservationsSensor.AddObservation(observation);
|
|
}
|
|
|
|
/// <summary>
|
|
/// Adds an integer observation to the vector observations of the agent.
|
|
/// Increases the size of the agents vector observation by 1.
|
|
/// </summary>
|
|
/// <param name="observation">Observation.</param>
|
|
protected void AddVectorObs(int observation)
|
|
{
|
|
collectObservationsSensor.AddObservation(observation);
|
|
}
|
|
|
|
/// <summary>
|
|
/// Adds an Vector3 observation to the vector observations of the agent.
|
|
/// Increases the size of the agents vector observation by 3.
|
|
/// </summary>
|
|
/// <param name="observation">Observation.</param>
|
|
protected void AddVectorObs(Vector3 observation)
|
|
{
|
|
collectObservationsSensor.AddObservation(observation);
|
|
}
|
|
|
|
/// <summary>
|
|
/// Adds an Vector2 observation to the vector observations of the agent.
|
|
/// Increases the size of the agents vector observation by 2.
|
|
/// </summary>
|
|
/// <param name="observation">Observation.</param>
|
|
protected void AddVectorObs(Vector2 observation)
|
|
{
|
|
collectObservationsSensor.AddObservation(observation);
|
|
}
|
|
|
|
/// <summary>
|
|
/// Adds a collection of float observations to the vector observations of the agent.
|
|
/// Increases the size of the agents vector observation by size of the collection.
|
|
/// </summary>
|
|
/// <param name="observation">Observation.</param>
|
|
protected void AddVectorObs(IEnumerable<float> observation)
|
|
{
|
|
collectObservationsSensor.AddObservation(observation);
|
|
}
|
|
|
|
/// <summary>
|
|
/// Adds a quaternion observation to the vector observations of the agent.
|
|
/// Increases the size of the agents vector observation by 4.
|
|
/// </summary>
|
|
/// <param name="observation">Observation.</param>
|
|
protected void AddVectorObs(Quaternion observation)
|
|
{
|
|
collectObservationsSensor.AddObservation(observation);
|
|
}
|
|
|
|
/// <summary>
|
|
/// Adds a boolean observation to the vector observation of the agent.
|
|
/// Increases the size of the agent's vector observation by 1.
|
|
/// </summary>
|
|
/// <param name="observation"></param>
|
|
protected void AddVectorObs(bool observation)
|
|
{
|
|
collectObservationsSensor.AddObservation(observation);
|
|
}
|
|
|
|
protected void AddVectorObs(int observation, int range)
|
|
{
|
|
collectObservationsSensor.AddOneHotObservation(observation, range);
|
|
}
|
|
|
|
/// <summary>
|
|
/// Specifies the agent behavior at every step based on the provided
|
|
/// action.
|
|
/// </summary>
|
|
/// <param name="vectorAction">
|
|
/// Vector action. Note that for discrete actions, the provided array
|
|
/// will be of length 1.
|
|
/// </param>
|
|
public virtual void AgentAction(float[] vectorAction)
|
|
{
|
|
}
|
|
|
|
/// <summary>
|
|
/// Specifies the agent behavior when done and
|
|
/// <see cref="AgentParameters.resetOnDone"/> is false. This method can be
|
|
/// used to remove the agent from the scene.
|
|
/// </summary>
|
|
public virtual void AgentOnDone()
|
|
{
|
|
}
|
|
|
|
/// <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>
|
|
/// 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()
|
|
{
|
|
m_HasAlreadyReset = false;
|
|
_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();
|
|
}
|
|
|
|
public void UpdateAgentAction(AgentAction action)
|
|
{
|
|
m_Action = action;
|
|
}
|
|
|
|
/// <summary>
|
|
/// Updates the vector action.
|
|
/// </summary>
|
|
/// <param name="vectorActions">Vector actions.</param>
|
|
public void UpdateVectorAction(float[] vectorActions)
|
|
{
|
|
m_Action.vectorActions = vectorActions;
|
|
}
|
|
|
|
/// <summary>
|
|
/// Updates the value of the agent.
|
|
/// </summary>
|
|
public void UpdateValueAction(float value)
|
|
{
|
|
m_Action.value = value;
|
|
}
|
|
|
|
protected float GetValueEstimate()
|
|
{
|
|
return m_Action.value;
|
|
}
|
|
|
|
/// <summary>
|
|
/// Scales continuous action from [-1, 1] to arbitrary range.
|
|
/// </summary>
|
|
/// <param name="rawAction"></param>
|
|
/// <param name="min"></param>
|
|
/// <param name="max"></param>
|
|
/// <returns></returns>
|
|
protected float ScaleAction(float rawAction, float min, float max)
|
|
{
|
|
var middle = (min + max) / 2;
|
|
var range = (max - min) / 2;
|
|
return rawAction * range + middle;
|
|
}
|
|
|
|
/// <summary>
|
|
/// Sets the status of the agent. Will request decisions or actions according
|
|
/// to the Academy's stepcount.
|
|
/// </summary>
|
|
/// <param name="academyStepCounter">Number of current steps in episode</param>
|
|
void SetStatus(int academyStepCounter)
|
|
{
|
|
MakeRequests(academyStepCounter);
|
|
}
|
|
|
|
/// Signals the agent that it must reset if its done flag is set to true.
|
|
void ResetIfDone()
|
|
{
|
|
// If an agent is done, then it will also
|
|
// request for a decision and an action
|
|
if (IsDone())
|
|
{
|
|
if (agentParameters.resetOnDone)
|
|
{
|
|
if (agentParameters.onDemandDecision)
|
|
{
|
|
if (!m_HasAlreadyReset)
|
|
{
|
|
// If event based, the agent can reset as soon
|
|
// as it is done
|
|
_AgentReset();
|
|
m_HasAlreadyReset = true;
|
|
}
|
|
}
|
|
else if (m_RequestDecision)
|
|
{
|
|
// If not event based, the agent must wait to request a
|
|
// decision before resetting to keep multiple agents in sync.
|
|
_AgentReset();
|
|
}
|
|
}
|
|
else
|
|
{
|
|
m_Terminate = true;
|
|
RequestDecision();
|
|
}
|
|
}
|
|
}
|
|
|
|
/// <summary>
|
|
/// Signals the agent that it must sent its decision to the brain.
|
|
/// </summary>
|
|
void SendInfo()
|
|
{
|
|
if (m_RequestDecision)
|
|
{
|
|
SendInfoToBrain();
|
|
ResetReward();
|
|
m_Done = false;
|
|
m_MaxStepReached = false;
|
|
m_RequestDecision = false;
|
|
|
|
m_HasAlreadyReset = false;
|
|
}
|
|
}
|
|
|
|
/// Used by the brain to make the agent perform a step.
|
|
void AgentStep()
|
|
{
|
|
if (m_Terminate)
|
|
{
|
|
m_Terminate = false;
|
|
ResetReward();
|
|
m_Done = false;
|
|
m_MaxStepReached = false;
|
|
m_RequestDecision = false;
|
|
m_RequestAction = false;
|
|
|
|
m_HasAlreadyReset = false;
|
|
OnDisable();
|
|
AgentOnDone();
|
|
}
|
|
|
|
if ((m_RequestAction) && (m_Brain != null))
|
|
{
|
|
m_RequestAction = false;
|
|
AgentAction(m_Action.vectorActions);
|
|
}
|
|
|
|
if ((m_StepCount >= agentParameters.maxStep)
|
|
&& (agentParameters.maxStep > 0))
|
|
{
|
|
m_MaxStepReached = true;
|
|
Done();
|
|
}
|
|
|
|
m_StepCount += 1;
|
|
}
|
|
|
|
/// <summary>
|
|
/// Is called after every step, contains the logic to decide if the agent
|
|
/// will request a decision at the next step.
|
|
/// </summary>
|
|
void MakeRequests(int academyStepCounter)
|
|
{
|
|
agentParameters.numberOfActionsBetweenDecisions =
|
|
Mathf.Max(agentParameters.numberOfActionsBetweenDecisions, 1);
|
|
if (!agentParameters.onDemandDecision)
|
|
{
|
|
RequestAction();
|
|
if (academyStepCounter %
|
|
agentParameters.numberOfActionsBetweenDecisions == 0)
|
|
{
|
|
RequestDecision();
|
|
}
|
|
}
|
|
}
|
|
|
|
void DecideAction()
|
|
{
|
|
m_Brain?.DecideAction();
|
|
}
|
|
}
|
|
}
|