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1038 行
38 KiB
1038 行
38 KiB
using System.Collections.Generic;
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using System.Linq;
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using UnityEngine;
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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 agent vector (i.e. numeric) observation.
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/// </summary>
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public List<float> vectorObservation;
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/// <summary>
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/// The previous agent vector observations, stacked. The length of the
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/// history (i.e. number of vector observations to stack) is specified
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/// in the Brain parameters.
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/// </summary>
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public List<float> stackedVectorObservation;
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/// <summary>
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/// Most recent agent camera (i.e. texture) observation.
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/// </summary>
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public List<Texture2D> visualObservations;
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/// <summary>
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/// Most recent text observation.
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/// </summary>
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public string textObservation;
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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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/// Keeps track of the last text action taken by the Brain.
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/// </summary>
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public string storedTextActions;
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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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/// Used by the Trainer to store information about the agent. This data
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/// structure is not consumed or modified by the agent directly, they are
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/// just the owners of their trainier's memory. Currently, however, the
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/// size of the memory is in the Brain properties.
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/// </summary>
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public List<float> memories;
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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 string textActions;
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public List<float> memories;
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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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[System.Serializable]
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public class AgentParameters
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{
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/// <summary>
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/// The list of the Camera GameObjects the agent uses for visual
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/// observations.
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/// </summary>
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public List<Camera> agentCameras = new List<Camera>();
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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 linked Brain. 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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/// linked brain and in return receives an action from its brain. 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 sucessive steps. Currently, how often an
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/// agent updates its brain with a fresh observation is determined by the
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/// Academy.
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///
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/// At any step, an agent may be considered <see cref="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 brain linked to the agent is allowed to
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/// change programmatically with <see cref="GiveBrain"/>.
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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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[System.Serializable]
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public abstract class Agent : MonoBehaviour
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{
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/// <summary>
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/// The Brain attached to this agent. A brain can be attached either
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/// directly from the Editor through AgentEditor or
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/// programmatically through <see cref="GiveBrain"/>. It is OK for an agent
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/// to not have a brain, as long as no decision is requested.
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/// </summary>
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[HideInInspector] public Brain brain;
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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 info;
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/// Current Agent action (message sent from Brain).
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AgentAction 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 reward;
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/// Keeps track of the cumulative reward in this episode.
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float cumulativeReward;
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/// Whether or not the agent requests an action.
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bool requestAction;
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/// Whether or not the agent requests a decision.
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bool 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 done;
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/// Whether or not the agent reached the maximum number of steps.
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bool 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 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 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 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 id;
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/// Keeps track of the actions that are masked at each step.
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private ActionMasker actionMasker;
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/// Array of Texture2D used to render to from render buffer before
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/// transforming into float tensor.
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Texture2D[] textureArray;
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/// Monobehavior 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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textureArray = new Texture2D[agentParameters.agentCameras.Count];
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for (int i = 0; i < agentParameters.agentCameras.Count; i++)
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{
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textureArray[i] = new Texture2D(1, 1, TextureFormat.RGB24, false);
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}
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id = gameObject.GetInstanceID();
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Academy academy = Object.FindObjectOfType<Academy>() as Academy;
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OnEnableHelper(academy);
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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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info = new AgentInfo();
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action = new AgentAction();
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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.AgentAct += AgentStep;
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academy.AgentForceReset += _AgentReset;
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if (brain != null)
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{
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ResetData();
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}
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else
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{
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Debug.Log(
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string.Format(
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"The Agent component attached to the " +
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"GameObject {0} was initialized without a brain.",
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gameObject.name));
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}
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InitializeAgent();
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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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Academy academy = Object.FindObjectOfType<Academy>() as 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.AgentAct -= AgentStep;
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academy.AgentForceReset -= _AgentReset;
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}
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}
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/// <summary>
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/// Updates the Brain for the agent. Any brain currently assigned to the
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/// agent will be replaced with the provided one.
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/// </summary>
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/// <remarks>
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/// The agent unsubscribes from its current brain (if it has one) and
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/// subscribes to the provided brain. This enables contextual brains, that
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/// is, updating the behaviour (hence brain) of the agent depending on
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/// the context of the game. For example, we may utilize one (wandering)
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/// brain when an agent is randomly exploring an open world, but switch
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/// to another (fighting) brain when it comes into contact with an enemy.
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/// </remarks>
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/// <param name="brain">New brain to subscribe this agent to</param>
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public void GiveBrain(Brain brain)
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{
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this.brain = brain;
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ResetData();
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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 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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reward = 0f;
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if (done)
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{
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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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cumulativeReward += (reward - this.reward);
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this.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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reward += increment;
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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 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 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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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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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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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 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 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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if (brain == null)
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{
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return;
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}
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BrainParameters param = brain.brainParameters;
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actionMasker = new ActionMasker(param);
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if (param.vectorActionSpaceType == SpaceType.continuous)
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{
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action.vectorActions = new float[param.vectorActionSize[0]];
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info.storedVectorActions = new float[param.vectorActionSize[0]];
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}
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else
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{
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action.vectorActions = new float[param.vectorActionSize.Length];
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info.storedVectorActions = new float[param.vectorActionSize.Length];
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}
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if (info.textObservation == null)
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info.textObservation = "";
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action.textActions = "";
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info.memories = new List<float>();
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action.memories = new List<float>();
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info.vectorObservation =
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new List<float>(param.vectorObservationSize);
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info.stackedVectorObservation =
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new List<float>(param.vectorObservationSize
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* brain.brainParameters.numStackedVectorObservations);
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info.stackedVectorObservation.AddRange(
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new float[param.vectorObservationSize
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* param.numStackedVectorObservations]);
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info.visualObservations = new List<Texture2D>();
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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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/// Sends the Agent info to the linked Brain.
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/// </summary>
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void SendInfoToBrain()
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{
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if (brain == null)
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{
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return;
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}
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info.memories = action.memories;
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info.storedVectorActions = action.vectorActions;
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info.storedTextActions = action.textActions;
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info.vectorObservation.Clear();
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actionMasker.ResetMask();
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CollectObservations();
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info.actionMasks = actionMasker.GetMask();
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BrainParameters param = brain.brainParameters;
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if (info.vectorObservation.Count != param.vectorObservationSize)
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{
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throw new UnityAgentsException(string.Format(
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"Vector Observation size mismatch between continuous " +
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"agent {0} and brain {1}. " +
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"Was Expecting {2} but received {3}. ",
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gameObject.name, brain.gameObject.name,
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brain.brainParameters.vectorObservationSize,
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info.vectorObservation.Count));
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}
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info.stackedVectorObservation.RemoveRange(
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0, param.vectorObservationSize);
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info.stackedVectorObservation.AddRange(info.vectorObservation);
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info.visualObservations.Clear();
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if (param.cameraResolutions.Length > agentParameters.agentCameras.Count)
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{
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throw new UnityAgentsException(string.Format(
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|
"Not enough cameras for agent {0} : Bain {1} expecting at " +
|
|
"least {2} cameras but only {3} were present.",
|
|
gameObject.name, brain.gameObject.name,
|
|
brain.brainParameters.cameraResolutions.Length,
|
|
agentParameters.agentCameras.Count));
|
|
}
|
|
|
|
for (int i = 0; i < brain.brainParameters.cameraResolutions.Length; i++)
|
|
{
|
|
ObservationToTexture(
|
|
agentParameters.agentCameras[i],
|
|
param.cameraResolutions[i].width,
|
|
param.cameraResolutions[i].height,
|
|
ref textureArray[i]);
|
|
info.visualObservations.Add(textureArray[i]);
|
|
}
|
|
|
|
info.reward = reward;
|
|
info.done = done;
|
|
info.maxStepReached = maxStepReached;
|
|
info.id = id;
|
|
|
|
brain.SendState(this, info);
|
|
info.textObservation = "";
|
|
}
|
|
|
|
/// <summary>
|
|
/// Collects the (vector, visual, text) 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, visual or textual 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[])"/>
|
|
/// - <see cref="AddVectorObs(List{float})"/>
|
|
/// - <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.
|
|
/// Lastly, textual observations are added using
|
|
/// <see cref="SetTextObs(string)"/>.
|
|
/// </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)
|
|
{
|
|
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)
|
|
{
|
|
actionMasker.SetActionMask(0, new int[1]{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)
|
|
{
|
|
actionMasker.SetActionMask(branch, new int[1]{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)
|
|
{
|
|
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)
|
|
{
|
|
info.vectorObservation.Add(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)
|
|
{
|
|
info.vectorObservation.Add(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)
|
|
{
|
|
info.vectorObservation.Add(observation.x);
|
|
info.vectorObservation.Add(observation.y);
|
|
info.vectorObservation.Add(observation.z);
|
|
}
|
|
|
|
/// <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)
|
|
{
|
|
info.vectorObservation.Add(observation.x);
|
|
info.vectorObservation.Add(observation.y);
|
|
}
|
|
|
|
/// <summary>
|
|
/// Adds a float array observation to the vector observations of the agent.
|
|
/// Increases the size of the agents vector observation by size of array.
|
|
/// </summary>
|
|
/// <param name="observation">Observation.</param>
|
|
protected void AddVectorObs(float[] observation)
|
|
{
|
|
info.vectorObservation.AddRange(observation);
|
|
}
|
|
|
|
/// <summary>
|
|
/// Adds a float list observation to the vector observations of the agent.
|
|
/// Increases the size of the agents vector observation by size of list.
|
|
/// </summary>
|
|
/// <param name="observation">Observation.</param>
|
|
protected void AddVectorObs(List<float> observation)
|
|
{
|
|
info.vectorObservation.AddRange(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)
|
|
{
|
|
info.vectorObservation.Add(observation.x);
|
|
info.vectorObservation.Add(observation.y);
|
|
info.vectorObservation.Add(observation.z);
|
|
info.vectorObservation.Add(observation.w);
|
|
}
|
|
|
|
/// <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)
|
|
{
|
|
info.vectorObservation.Add(observation ? 1f : 0f);
|
|
}
|
|
|
|
protected void AddVectorObs(int observation, int range)
|
|
{
|
|
float[] oneHotVector = new float[range];
|
|
oneHotVector[observation] = 1;
|
|
info.vectorObservation.AddRange(oneHotVector);
|
|
}
|
|
|
|
/// <summary>
|
|
/// Sets the text observation.
|
|
/// </summary>
|
|
/// <param name="textObservation">The text observation.</param>
|
|
public void SetTextObs(string textObservation)
|
|
{
|
|
info.textObservation = textObservation;
|
|
}
|
|
|
|
/// <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>
|
|
/// <param name="textAction">Text action.</param>
|
|
public virtual void AgentAction(float[] vectorAction, string textAction)
|
|
{
|
|
|
|
}
|
|
|
|
/// <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>
|
|
/// An internal reset method that updates internal data structures in
|
|
/// addition to calling <see cref="AgentReset"/>.
|
|
/// </summary>
|
|
void _AgentReset()
|
|
{
|
|
ResetData();
|
|
stepCount = 0;
|
|
AgentReset();
|
|
}
|
|
|
|
/// <summary>
|
|
/// Updates the vector action.
|
|
/// </summary>
|
|
/// <param name="vectorActions">Vector actions.</param>
|
|
public void UpdateVectorAction(float[] vectorActions)
|
|
{
|
|
action.vectorActions = vectorActions;
|
|
}
|
|
|
|
/// <summary>
|
|
/// Updates the memories action.
|
|
/// </summary>
|
|
/// <param name="memories">Memories.</param>
|
|
public void UpdateMemoriesAction(List<float> memories)
|
|
{
|
|
action.memories = memories;
|
|
}
|
|
|
|
/// <summary>
|
|
/// Updates the text action.
|
|
/// </summary>
|
|
/// <param name="textActions">Text actions.</param>
|
|
public void UpdateTextAction(string textActions)
|
|
{
|
|
action.textActions = textActions;
|
|
}
|
|
|
|
/// <summary>
|
|
/// Updates the value of the agent.
|
|
/// </summary>
|
|
/// <param name="textActions">Text actions.</param>
|
|
public void UpdateValueAction(float value)
|
|
{
|
|
action.value = value;
|
|
}
|
|
|
|
protected float GetValueEstimate()
|
|
{
|
|
return action.value;
|
|
}
|
|
|
|
/// <summary>
|
|
/// Scales continous 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.
|
|
/// </summary>
|
|
/// <param name="academyMaxStep">If set to <c>true</c>
|
|
/// The agent must set maxStepReached.</param>
|
|
/// <param name="academyDone">If set to <c>true</c>
|
|
/// The agent must set done.</param>
|
|
/// <param name="academyStepCounter">Number of current steps in episode</param>
|
|
void SetStatus(bool academyMaxStep, bool academyDone, int academyStepCounter)
|
|
{
|
|
if (academyDone)
|
|
{
|
|
academyStepCounter = 0;
|
|
}
|
|
|
|
MakeRequests(academyStepCounter);
|
|
if (academyMaxStep)
|
|
{
|
|
maxStepReached = true;
|
|
}
|
|
|
|
// If the Academy needs to reset, the agent should reset
|
|
// even if it reseted recently.
|
|
if (academyDone)
|
|
{
|
|
Done();
|
|
hasAlreadyReset = false;
|
|
}
|
|
}
|
|
|
|
/// 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 (!hasAlreadyReset)
|
|
{
|
|
// If event based, the agent can reset as soon
|
|
// as it is done
|
|
_AgentReset();
|
|
hasAlreadyReset = true;
|
|
}
|
|
}
|
|
else if (requestDecision)
|
|
{
|
|
// If not event based, the agent must wait to request a
|
|
// decsion before reseting to keep multiple agents in sync.
|
|
_AgentReset();
|
|
}
|
|
}
|
|
else
|
|
{
|
|
terminate = true;
|
|
RequestDecision();
|
|
}
|
|
}
|
|
}
|
|
|
|
/// <summary>
|
|
/// Signals the agent that it must sent its decision to the brain.
|
|
/// </summary>
|
|
void SendInfo()
|
|
{
|
|
if (requestDecision)
|
|
{
|
|
SendInfoToBrain();
|
|
ResetReward();
|
|
done = false;
|
|
maxStepReached = false;
|
|
requestDecision = false;
|
|
|
|
hasAlreadyReset = false;
|
|
}
|
|
}
|
|
|
|
/// Used by the brain to make the agent perform a step.
|
|
void AgentStep()
|
|
{
|
|
if (terminate)
|
|
{
|
|
terminate = false;
|
|
ResetReward();
|
|
done = false;
|
|
maxStepReached = false;
|
|
requestDecision = false;
|
|
requestAction = false;
|
|
|
|
hasAlreadyReset = false;
|
|
OnDisable();
|
|
AgentOnDone();
|
|
}
|
|
|
|
if ((requestAction) && (brain != null))
|
|
{
|
|
requestAction = false;
|
|
AgentAction(action.vectorActions, action.textActions);
|
|
}
|
|
|
|
if ((stepCount >= agentParameters.maxStep)
|
|
&& (agentParameters.maxStep > 0))
|
|
{
|
|
maxStepReached = true;
|
|
Done();
|
|
}
|
|
|
|
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();
|
|
}
|
|
}
|
|
}
|
|
|
|
/// <summary>
|
|
/// Converts a camera and correspinding resolution to a 2D texture.
|
|
/// </summary>
|
|
/// <returns>The 2D texture.</returns>
|
|
/// <param name="obsCamera">Camera.</param>
|
|
/// <param name="width">Width of resulting 2D texture.</param>
|
|
/// <param name="height">Height of resulting 2D texture.</param>
|
|
/// <param name="texture2D">Texture2D to render to.</param>
|
|
public static void ObservationToTexture(Camera obsCamera, int width, int height, ref Texture2D texture2D)
|
|
{
|
|
Rect oldRec = obsCamera.rect;
|
|
obsCamera.rect = new Rect(0f, 0f, 1f, 1f);
|
|
var depth = 24;
|
|
var format = RenderTextureFormat.Default;
|
|
var readWrite = RenderTextureReadWrite.Default;
|
|
|
|
var tempRT =
|
|
RenderTexture.GetTemporary(width, height, depth, format, readWrite);
|
|
|
|
if (width != texture2D.width || height != texture2D.height)
|
|
{
|
|
texture2D.Resize(width, height);
|
|
}
|
|
|
|
var prevActiveRT = RenderTexture.active;
|
|
var prevCameraRT = obsCamera.targetTexture;
|
|
|
|
// render to offscreen texture (readonly from CPU side)
|
|
RenderTexture.active = tempRT;
|
|
obsCamera.targetTexture = tempRT;
|
|
|
|
obsCamera.Render();
|
|
|
|
texture2D.ReadPixels(new Rect(0, 0, texture2D.width, texture2D.height), 0, 0);
|
|
texture2D.Apply();
|
|
obsCamera.targetTexture = prevCameraRT;
|
|
obsCamera.rect = oldRec;
|
|
RenderTexture.active = prevActiveRT;
|
|
RenderTexture.ReleaseTemporary(tempRT);
|
|
}
|
|
}
|
|
}
|