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653 行
23 KiB
653 行
23 KiB
using System;
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using UnityEngine;
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using System.Collections.Generic;
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#if UNITY_EDITOR
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using UnityEditor;
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#endif
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using Unity.MLAgents.Actuators;
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using Unity.MLAgents.Inference;
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using Unity.MLAgents.Policies;
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using Unity.MLAgents.SideChannels;
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using Unity.Barracuda;
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/**
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* Welcome to Unity Machine Learning Agents (ML-Agents).
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*
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* The ML-Agents toolkit contains four entities: Academy, Agent, Communicator and
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* Python API. The academy and connected agents live within
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* a learning environment (herein called Environment), while the communicator
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* manages the communication between the learning environment and the Python
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* API. For more information on each of these entities, in addition to how to
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* set-up a learning environment and train the behavior of characters in a
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* Unity scene, please browse our documentation pages on GitHub:
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* https://github.com/Unity-Technologies/ml-agents/tree/release_10_docs/docs/
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*/
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namespace Unity.MLAgents
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{
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/// <summary>
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/// Helper class to step the Academy during FixedUpdate phase.
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/// </summary>
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internal class AcademyFixedUpdateStepper : MonoBehaviour
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{
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void FixedUpdate()
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{
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// Check if the stepper belongs to the current Academy and destroy it if it's not.
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// This is to prevent from having leaked stepper from previous runs.
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if (!Academy.IsInitialized || !Academy.Instance.IsStepperOwner(this))
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{
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Destroy(this.gameObject);
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}
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else
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{
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Academy.Instance.EnvironmentStep();
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}
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}
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}
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/// <summary>
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/// The Academy singleton manages agent training and decision making.
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/// </summary>
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/// <remarks>
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/// Access the Academy singleton through the <see cref="Instance"/>
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/// property. The Academy instance is initialized the first time it is accessed (which will
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/// typically be by the first <see cref="Agent"/> initialized in a scene).
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///
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/// At initialization, the Academy attempts to connect to the Python training process through
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/// the external communicator. If successful, the training process can train <see cref="Agent"/>
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/// instances. When you set an agent's <see cref="BehaviorParameters.BehaviorType"/> setting
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/// to <see cref="BehaviorType.Default"/>, the agent exchanges data with the training process
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/// to make decisions. If no training process is available, agents with the default behavior
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/// fall back to inference or heuristic decisions. (You can also set agents to always use
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/// inference or heuristics.)
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/// </remarks>
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[HelpURL("https://github.com/Unity-Technologies/ml-agents/tree/release_10_docs/" +
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"docs/Learning-Environment-Design.md")]
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public class Academy : IDisposable
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{
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/// <summary>
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/// Communication protocol version.
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/// When connecting to python, this must be compatible with UnityEnvironment.API_VERSION.
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/// We follow semantic versioning on the communication version, so existing
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/// functionality will work as long the major versions match.
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/// This should be changed whenever a change is made to the communication protocol.
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/// </summary>
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/// <remarks>
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/// History:
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/// <list type="bullet">
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/// <item>
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/// <term>1.0.0</term>
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/// <description>Initial version</description>
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/// </item>
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/// <item>
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/// <term>1.1.0</term>
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/// <description>Support concatenated PNGs for compressed observations.</description>
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/// </item>
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/// <item>
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/// <term>1.2.0</term>
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/// <description>Support compression mapping for stacked compressed observations.</description>
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/// </item>
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/// </list>
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/// </remarks>
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const string k_ApiVersion = "1.2.0";
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/// <summary>
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/// Unity package version of com.unity.ml-agents.
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/// This must match the version string in package.json and is checked in a unit test.
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/// </summary>
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internal const string k_PackageVersion = "1.6.0-preview";
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const int k_EditorTrainingPort = 5004;
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const string k_PortCommandLineFlag = "--mlagents-port";
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// Lazy initializer pattern, see https://csharpindepth.com/articles/singleton#lazy
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static Lazy<Academy> s_Lazy = new Lazy<Academy>(() => new Academy());
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/// <summary>
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///Reports whether the Academy has been initialized yet.
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/// </summary>
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/// <value><c>True</c> if the Academy is initialized, <c>false</c> otherwise.</value>
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public static bool IsInitialized
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{
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get { return s_Lazy.IsValueCreated; }
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}
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/// <summary>
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/// The singleton Academy object.
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/// </summary>
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/// <value>Getting the instance initializes the Academy, if necessary.</value>
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public static Academy Instance { get { return s_Lazy.Value; } }
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// Fields not provided in the Inspector.
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/// <summary>
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/// Reports whether or not the communicator is on.
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/// </summary>
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/// <seealso cref="ICommunicator"/>
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/// <value>
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/// <c>True</c>, if communicator is on, <c>false</c> otherwise.
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/// </value>
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public bool IsCommunicatorOn
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{
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get { return Communicator != null; }
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}
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/// The number of episodes completed by the environment. Incremented
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/// each time the environment is reset.
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int m_EpisodeCount;
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/// The number of steps completed within the current episode. Incremented
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/// each time a step is taken in the environment. Is reset to 0 during
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/// <see cref="EnvironmentReset"/>.
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int m_StepCount;
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/// The number of total number of steps completed during the whole simulation. Incremented
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/// each time a step is taken in the environment.
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int m_TotalStepCount;
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/// Pointer to the communicator currently in use by the Academy.
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internal ICommunicator Communicator;
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bool m_Initialized;
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List<ModelRunner> m_ModelRunners = new List<ModelRunner>();
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// Flag used to keep track of the first time the Academy is reset.
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bool m_HadFirstReset;
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// Detect an Academy step called by user code that is also called by the Academy.
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private RecursionChecker m_StepRecursionChecker = new RecursionChecker("EnvironmentStep");
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// Random seed used for inference.
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int m_InferenceSeed;
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/// <summary>
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/// Set the random seed used for inference. This should be set before any Agents are added
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/// to the scene. The seed is passed to the ModelRunner constructor, and incremented each
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/// time a new ModelRunner is created.
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/// </summary>
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public int InferenceSeed
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{
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set { m_InferenceSeed = value; }
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}
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/// <summary>
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/// Returns the RLCapabilities of the python client that the unity process is connected to.
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/// </summary>
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internal UnityRLCapabilities TrainerCapabilities { get; set; }
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// The Academy uses a series of events to communicate with agents
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// to facilitate synchronization. More specifically, it ensures
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// that all the agents perform their steps in a consistent order (i.e. no
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// agent can act based on a decision before another agent has had a chance
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// to request a decision).
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// Signals to all the Agents at each environment step so they can use
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// their Policy to decide on their next action.
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internal event Action DecideAction;
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// Signals to all the listeners that the academy is being destroyed
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internal event Action DestroyAction;
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// Signals to the Agent that a new step is about to start.
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// This will mark the Agent as Done if it has reached its maxSteps.
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internal event Action AgentIncrementStep;
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/// <summary>
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/// Signals to all of the <see cref="Agent"/>s that their step is about to begin.
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/// This is a good time for an <see cref="Agent"/> to decide if it would like to
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/// call <see cref="Agent.RequestDecision"/> or <see cref="Agent.RequestAction"/>
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/// for this step. Any other pre-step setup could be done during this even as well.
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/// </summary>
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public event Action<int> AgentPreStep;
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// Signals to all the agents at each environment step so they can send
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// their state to their Policy if they have requested a decision.
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internal event Action AgentSendState;
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// Signals to all the agents at each environment step so they can act if
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// they have requested a decision.
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internal event Action AgentAct;
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// Signals to all the agents each time the Academy force resets.
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internal event Action AgentForceReset;
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/// <summary>
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/// Signals that the Academy has been reset by the training process.
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/// </summary>
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public event Action OnEnvironmentReset;
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AcademyFixedUpdateStepper m_FixedUpdateStepper;
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GameObject m_StepperObject;
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/// <summary>
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/// Private constructor called the first time the Academy is used.
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/// Academy uses this time to initialize internal data
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/// structures, initialize the environment and check for the existence
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/// of a communicator.
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/// </summary>
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Academy()
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{
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Application.quitting += Dispose;
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LazyInitialize();
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#if UNITY_EDITOR
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EditorApplication.playModeStateChanged += HandleOnPlayModeChanged;
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#endif
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}
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#if UNITY_EDITOR
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/// <summary>
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/// Clean up the Academy when switching from edit mode to play mode
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/// </summary>
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/// <param name="state">State.</param>
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void HandleOnPlayModeChanged(PlayModeStateChange state)
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{
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if (state == PlayModeStateChange.ExitingEditMode)
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{
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Dispose();
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}
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}
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#endif
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/// <summary>
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/// Initialize the Academy if it hasn't already been initialized.
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/// This method is always safe to call; it will have no effect if the Academy is already
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/// initialized.
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/// </summary>
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internal void LazyInitialize()
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{
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if (!m_Initialized)
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{
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InitializeEnvironment();
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m_Initialized = true;
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}
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}
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/// <summary>
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/// Enable stepping of the Academy during the FixedUpdate phase. This is done by creating
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/// a temporary GameObject with a MonoBehaviour that calls Academy.EnvironmentStep().
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/// </summary>
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void EnableAutomaticStepping()
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{
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if (m_FixedUpdateStepper != null)
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{
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return;
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}
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m_StepperObject = new GameObject("AcademyFixedUpdateStepper");
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// Don't show this object in the hierarchy
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m_StepperObject.hideFlags = HideFlags.HideInHierarchy;
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m_FixedUpdateStepper = m_StepperObject.AddComponent<AcademyFixedUpdateStepper>();
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try
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{
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// This try-catch is because DontDestroyOnLoad cannot be used in Editor Tests
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GameObject.DontDestroyOnLoad(m_StepperObject);
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}
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catch { }
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}
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/// <summary>
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/// Disable stepping of the Academy during the FixedUpdate phase. If this is called, the Academy must be
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/// stepped manually by the user by calling Academy.EnvironmentStep().
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/// </summary>
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void DisableAutomaticStepping()
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{
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if (m_FixedUpdateStepper == null)
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{
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return;
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}
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m_FixedUpdateStepper = null;
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if (Application.isEditor)
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{
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UnityEngine.Object.DestroyImmediate(m_StepperObject);
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}
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else
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{
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UnityEngine.Object.Destroy(m_StepperObject);
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}
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m_StepperObject = null;
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}
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/// <summary>
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/// Determines whether or not the Academy is automatically stepped during the FixedUpdate phase.
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/// </summary>
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/// <value>Set <c>true</c> to enable automatic stepping; <c>false</c> to disable.</value>
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public bool AutomaticSteppingEnabled
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{
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get { return m_FixedUpdateStepper != null; }
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set
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{
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if (value)
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{
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EnableAutomaticStepping();
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}
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else
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{
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DisableAutomaticStepping();
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}
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}
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}
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// Used to read Python-provided environment parameters
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static int ReadPortFromArgs()
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{
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var args = Environment.GetCommandLineArgs();
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var inputPort = "";
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for (var i = 0; i < args.Length; i++)
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{
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if (args[i] == k_PortCommandLineFlag)
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{
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inputPort = args[i + 1];
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}
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}
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try
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{
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return int.Parse(inputPort);
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}
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catch
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{
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// No arg passed, or malformed port number.
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#if UNITY_EDITOR
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// Try connecting on the default editor port
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return k_EditorTrainingPort;
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#else
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// This is an executable, so we don't try to connect.
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return -1;
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#endif
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}
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}
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EnvironmentParameters m_EnvironmentParameters;
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StatsRecorder m_StatsRecorder;
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/// <summary>
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/// Returns the <see cref="EnvironmentParameters"/> instance. If training
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/// features such as Curriculum Learning or Environment Parameter Randomization are used,
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/// then the values of the parameters generated from the training process can be
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/// retrieved here.
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/// </summary>
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/// <returns></returns>
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public EnvironmentParameters EnvironmentParameters
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{
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get { return m_EnvironmentParameters; }
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}
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/// <summary>
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/// Returns the <see cref="StatsRecorder"/> instance. This instance can be used
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/// to record any statistics from the Unity environment.
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/// </summary>
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/// <returns></returns>
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public StatsRecorder StatsRecorder
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{
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get { return m_StatsRecorder; }
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}
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/// <summary>
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/// Initializes the environment, configures it and initializes the Academy.
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/// </summary>
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void InitializeEnvironment()
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{
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TimerStack.Instance.AddMetadata("communication_protocol_version", k_ApiVersion);
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TimerStack.Instance.AddMetadata("com.unity.ml-agents_version", k_PackageVersion);
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EnableAutomaticStepping();
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SideChannelManager.RegisterSideChannel(new EngineConfigurationChannel());
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m_EnvironmentParameters = new EnvironmentParameters();
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m_StatsRecorder = new StatsRecorder();
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// Try to launch the communicator by using the arguments passed at launch
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var port = ReadPortFromArgs();
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if (port > 0)
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{
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Communicator = new RpcCommunicator(
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new CommunicatorInitParameters
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{
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port = port
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}
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);
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}
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if (Communicator != null)
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{
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// We try to exchange the first message with Python. If this fails, it means
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// no Python Process is ready to train the environment. In this case, the
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//environment must use Inference.
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try
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{
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var unityRlInitParameters = Communicator.Initialize(
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new CommunicatorInitParameters
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{
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unityCommunicationVersion = k_ApiVersion,
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unityPackageVersion = k_PackageVersion,
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name = "AcademySingleton",
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CSharpCapabilities = new UnityRLCapabilities()
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});
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UnityEngine.Random.InitState(unityRlInitParameters.seed);
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// We might have inference-only Agents, so set the seed for them too.
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m_InferenceSeed = unityRlInitParameters.seed;
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TrainerCapabilities = unityRlInitParameters.TrainerCapabilities;
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TrainerCapabilities.WarnOnPythonMissingBaseRLCapabilities();
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}
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catch
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{
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Debug.Log($"" +
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$"Couldn't connect to trainer on port {port} using API version {k_ApiVersion}. " +
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"Will perform inference instead."
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);
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Communicator = null;
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}
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}
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if (Communicator != null)
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{
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Communicator.QuitCommandReceived += OnQuitCommandReceived;
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Communicator.ResetCommandReceived += OnResetCommand;
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}
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// If a communicator is enabled/provided, then we assume we are in
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// training mode. In the absence of a communicator, we assume we are
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// in inference mode.
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ResetActions();
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}
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void ResetActions()
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{
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DecideAction = () => { };
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DestroyAction = () => { };
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AgentPreStep = i => { };
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AgentSendState = () => { };
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AgentAct = () => { };
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AgentForceReset = () => { };
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OnEnvironmentReset = () => { };
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}
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static void OnQuitCommandReceived()
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{
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#if UNITY_EDITOR
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EditorApplication.isPlaying = false;
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#endif
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Application.Quit();
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}
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void OnResetCommand()
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{
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ForcedFullReset();
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}
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/// <summary>
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/// The current episode count.
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/// </summary>
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/// <value>
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/// Current episode number.
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/// </value>
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public int EpisodeCount
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{
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get { return m_EpisodeCount; }
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}
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/// <summary>
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/// The current step count (within the current episode).
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/// </summary>
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/// <value>
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/// Current step count.
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/// </value>
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public int StepCount
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{
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get { return m_StepCount; }
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}
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/// <summary>
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/// Returns the total step count.
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/// </summary>
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/// <value>
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/// Total step count.
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/// </value>
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public int TotalStepCount
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{
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get { return m_TotalStepCount; }
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}
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/// <summary>
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/// Forces the full reset. The done flags are not affected. Is either
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/// called the first reset at inference and every external reset
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/// at training.
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/// </summary>
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void ForcedFullReset()
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{
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EnvironmentReset();
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AgentForceReset?.Invoke();
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m_HadFirstReset = true;
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}
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/// <summary>
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/// Performs a single environment update of the Academy and Agent
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/// objects within the environment.
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/// </summary>
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public void EnvironmentStep()
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{
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using (m_StepRecursionChecker.Start())
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{
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if (!m_HadFirstReset)
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{
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ForcedFullReset();
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}
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AgentPreStep?.Invoke(m_StepCount);
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m_StepCount += 1;
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m_TotalStepCount += 1;
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AgentIncrementStep?.Invoke();
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using (TimerStack.Instance.Scoped("AgentSendState"))
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{
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AgentSendState?.Invoke();
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}
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using (TimerStack.Instance.Scoped("DecideAction"))
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{
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DecideAction?.Invoke();
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}
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// If the communicator is not on, we need to clear the SideChannel sending queue
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if (!IsCommunicatorOn)
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{
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SideChannelManager.GetSideChannelMessage();
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}
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|
|
using (TimerStack.Instance.Scoped("AgentAct"))
|
|
{
|
|
AgentAct?.Invoke();
|
|
}
|
|
}
|
|
}
|
|
|
|
/// <summary>
|
|
/// Resets the environment, including the Academy.
|
|
/// </summary>
|
|
void EnvironmentReset()
|
|
{
|
|
m_StepCount = 0;
|
|
m_EpisodeCount++;
|
|
OnEnvironmentReset?.Invoke();
|
|
}
|
|
|
|
/// <summary>
|
|
/// Creates or retrieves an existing ModelRunner that uses the same
|
|
/// NNModel and the InferenceDevice as provided.
|
|
/// </summary>
|
|
/// <param name="model">The NNModel the ModelRunner must use.</param>
|
|
/// <param name="actionSpec"> Description of the action spaces for the Agent.</param>
|
|
/// <param name="inferenceDevice">
|
|
/// The inference device (CPU or GPU) the ModelRunner will use.
|
|
/// </param>
|
|
/// <returns> The ModelRunner compatible with the input settings.</returns>
|
|
internal ModelRunner GetOrCreateModelRunner(
|
|
NNModel model, ActionSpec actionSpec, InferenceDevice inferenceDevice)
|
|
{
|
|
var modelRunner = m_ModelRunners.Find(x => x.HasModel(model, inferenceDevice));
|
|
if (modelRunner == null)
|
|
{
|
|
modelRunner = new ModelRunner(model, actionSpec, inferenceDevice, m_InferenceSeed);
|
|
m_ModelRunners.Add(modelRunner);
|
|
m_InferenceSeed++;
|
|
}
|
|
return modelRunner;
|
|
}
|
|
|
|
/// <summary>
|
|
/// Shut down the Academy.
|
|
/// </summary>
|
|
public void Dispose()
|
|
{
|
|
DisableAutomaticStepping();
|
|
|
|
// Signal to listeners that the academy is being destroyed now
|
|
DestroyAction?.Invoke();
|
|
|
|
Communicator?.Dispose();
|
|
Communicator = null;
|
|
|
|
m_EnvironmentParameters.Dispose();
|
|
m_StatsRecorder.Dispose();
|
|
SideChannelManager.UnregisterAllSideChannels(); // unregister custom side channels
|
|
|
|
if (m_ModelRunners != null)
|
|
{
|
|
foreach (var mr in m_ModelRunners)
|
|
{
|
|
mr.Dispose();
|
|
}
|
|
|
|
m_ModelRunners = null;
|
|
}
|
|
|
|
// Clear out the actions so we're not keeping references to any old objects
|
|
ResetActions();
|
|
|
|
// TODO - Pass worker ID or some other identifier,
|
|
// so that multiple envs won't overwrite each others stats.
|
|
TimerStack.Instance.SaveJsonTimers();
|
|
m_Initialized = false;
|
|
|
|
// Reset the Lazy instance
|
|
s_Lazy = new Lazy<Academy>(() => new Academy());
|
|
}
|
|
|
|
/// <summary>
|
|
/// Check if the input AcademyFixedUpdateStepper belongs to this Academy.
|
|
/// </summary>
|
|
internal bool IsStepperOwner(AcademyFixedUpdateStepper stepper)
|
|
{
|
|
return GameObject.ReferenceEquals(stepper.gameObject, Academy.Instance.m_StepperObject);
|
|
}
|
|
}
|
|
}
|