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640 行
23 KiB
640 行
23 KiB
using System.Collections.Generic;
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using UnityEngine;
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using System.IO;
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using System.Linq;
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#if UNITY_EDITOR
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using UnityEditor;
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#endif
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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 five entities: Academy, Brain, Agent, Communicator and
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* Python API. The academy, and all its brains and connected agents live within
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* a learning environment (herin 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/blob/master/docs/
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*/
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namespace MLAgents
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{
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/// <summary>
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/// Wraps the environment-level parameters that are provided within the
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/// Editor. These parameters can be provided for training and inference
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/// modes separately and represent screen resolution, rendering quality and
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/// frame rate.
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/// </summary>
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[System.Serializable]
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public class EnvironmentConfiguration
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{
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[Tooltip("Width of the environment window in pixels.")]
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public int width;
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[Tooltip("Height of the environment window in pixels.")]
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public int height;
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[Tooltip("Rendering quality of environment. (Higher is better quality.)")] [Range(0, 5)]
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public int qualityLevel;
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[Tooltip("Speed at which environment is run. (Higher is faster.)")] [Range(1f, 100f)]
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public float timeScale;
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[Tooltip("Frames per second (FPS) engine attempts to maintain.")]
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public int targetFrameRate;
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/// Initializes a new instance of the
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/// <see cref="EnvironmentConfiguration"/> class.
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/// <param name="width">Width of environment window (pixels).</param>
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/// <param name="height">Height of environment window (pixels).</param>
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/// <param name="qualityLevel">
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/// Rendering quality of environment. Ranges from 0 to 5, with higher.
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/// </param>
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/// <param name="timeScale">
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/// Speed at which environment is run. Ranges from 1 to 100, with higher
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/// values representing faster speed.
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/// </param>
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/// <param name="targetFrameRate">
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/// Target frame rate (per second) that the engine tries to maintain.
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/// </param>
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public EnvironmentConfiguration(
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int width, int height, int qualityLevel,
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float timeScale, int targetFrameRate)
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{
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this.width = width;
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this.height = height;
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this.qualityLevel = qualityLevel;
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this.timeScale = timeScale;
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this.targetFrameRate = targetFrameRate;
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}
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}
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/// <summary>
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/// An Academy is where Agent objects go to train their behaviors. More
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/// specifically, an academy is a collection of Brain objects and each agent
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/// in a scene is attached to one brain (a single brain may be attached to
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/// multiple agents). Currently, this class is expected to be extended to
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/// implement the desired academy behavior.
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/// </summary>
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/// <remarks>
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/// When an academy is run, it can either be in inference or training mode.
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/// The mode is determined by the presence or absence of a Communicator. In
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/// the presence of a communicator, the academy is run in training mode where
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/// the states and observations of each agent are sent through the
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/// communicator. In the absence of a communciator, the academy is run in
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/// inference mode where the agent behavior is determined by the brain
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/// attached to it (which may be internal, heuristic or player).
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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-Academy.md")]
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public abstract class Academy : MonoBehaviour
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{
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private const string kApiVersion = "API-4";
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// Fields provided in the Inspector
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[SerializeField]
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[Tooltip("Total number of steps per global episode.\nNon-positive " +
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"values correspond to episodes without a maximum number of \n" +
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"steps. Once the step counter reaches this maximum value, the " +
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"environment will reset.")]
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int maxSteps;
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[SerializeField]
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[Tooltip("The engine-level settings which correspond to rendering " +
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"quality and engine speed during Training.")]
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EnvironmentConfiguration trainingConfiguration =
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new EnvironmentConfiguration(80, 80, 1, 100.0f, -1);
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[SerializeField]
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[Tooltip("The engine-level settings which correspond to rendering " +
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"quality and engine speed during Inference.")]
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EnvironmentConfiguration inferenceConfiguration =
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new EnvironmentConfiguration(1280, 720, 5, 1.0f, 60);
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/// <summary>
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/// Contains a mapping from parameter names to float values. They are
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/// used in <see cref="AcademyReset"/> and <see cref="AcademyStep"/>
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/// to modify elements in the environment at reset time.
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/// <summary/>
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/// <remarks>
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/// Default reset parameters are specified in the academy Editor, and can
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/// be modified when training with an external Brain by passinga config
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/// dictionary at reset.
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/// </remarks>
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[SerializeField]
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[Tooltip("List of custom parameters that can be changed in the " +
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"environment when it resets.")]
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public ResetParameters resetParameters;
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// Fields not provided in the Inspector.
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/// Boolean flag indicating whether a communicator is accessible by the
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/// environment. This also specifies whether the environment is in
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/// Training or Inference mode.
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bool isCommunicatorOn;
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/// Keeps track of the id of the last communicator message received.
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/// Remains 0 if there are no communicators. Is used to ensure that
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/// the same message is not used multiple times.
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private ulong lastCommunicatorMessageNumber;
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/// If true, the Academy will use inference settings. This field is
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/// initialized in <see cref="Awake"/> depending on the presence
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/// or absence of a communicator. Furthermore, it can be modified by an
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/// external Brain during reset via <see cref="SetIsInference"/>.
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bool isInference = true;
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/// The done flag of the academy. When set to true, the academy will
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/// call <see cref="AcademyReset"/> instead of <see cref="AcademyStep"/>
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/// at step time. If true, all agents done flags will be set to true.
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bool done;
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/// Whether the academy has reached the maximum number of steps for the
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/// current episode.
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bool maxStepReached;
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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 episodeCount;
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/// The number of steps completed within the current episide. 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="AcademyReset"/>.
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int stepCount;
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/// Flag that indicates whether the inference/training mode of the
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/// environment was switched by the external Brain. This impacts the
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/// engine settings at the next environment step.
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bool modeSwitched;
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/// Pointer to the batcher currently in use by the Academy.
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MLAgents.Batcher brainBatcher;
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/// Used to write error messages.
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StreamWriter logWriter;
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/// The path to where the log should be written.
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string logPath;
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// Flag used to keep track of the first time the Academy is reset.
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bool firstAcademyReset;
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// The Academy uses a series of events to communicate with agents and
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// brains to facilitate synchronization. More specifically, it ensure
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// that all the agents performs 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 Brains at each environment step so they can decide
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// actions for their agents.
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public event System.Action BrainDecideAction;
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// Signals to all the agents at each environment step along with the
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// Academy's maxStepReached, done and stepCount values. The agents rely
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// on this event to update their own values of max step reached and done
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// in addition to aligning on the step count of the global episode.
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public event System.Action<bool, bool, int> AgentSetStatus;
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// Signals to all the agents at each environment step so they can reset
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// if their flag has been set to done (assuming the agent has requested a
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// decision).
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public event System.Action AgentResetIfDone;
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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 Brain if they have requested a decision.
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public event System.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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public event System.Action AgentAct;
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// Sigals to all the agents each time the Academy force resets.
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public event System.Action AgentForceReset;
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/// <summary>
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/// Monobehavior function called at the very beginning of environment
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/// creation. 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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void Awake()
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{
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InitializeEnvironment();
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}
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// Used to read Python-provided environment parameters
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private int ReadArgs()
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{
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var args = System.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] == "--port")
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{
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inputPort = args[i + 1];
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}
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}
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return int.Parse(inputPort);
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}
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/// <summary>
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/// Initializes the environment, configures it and initialized the Academy.
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/// </summary>
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private void InitializeEnvironment()
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{
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// Retrieve Brain and initialize Academy
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var brains = GetBrains(gameObject);
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InitializeAcademy();
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MLAgents.Communicator communicator = null;
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// Try to launch the communicator by usig the arguments passed at launch
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try
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{
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communicator = new MLAgents.RPCCommunicator(
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new MLAgents.CommunicatorParameters
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{
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port = ReadArgs()
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});
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}
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// If it fails, we check if there are any external brains in the scene
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// If there are : Launch the communicator on the default port
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// If there arn't, there is no need for a communicator and it is set
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// to null
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catch
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{
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communicator = null;
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var externalBrain = brains.FirstOrDefault(b => b.brainType == BrainType.External);
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if (externalBrain != null)
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{
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communicator = new MLAgents.RPCCommunicator(
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new MLAgents.CommunicatorParameters
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{
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port = 5005
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});
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}
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}
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brainBatcher = new MLAgents.Batcher(communicator);
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// Initialize Brains and communicator (if present)
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foreach (var brain in brains)
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{
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brain.InitializeBrain(this, brainBatcher);
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}
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if (communicator != null)
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{
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isCommunicatorOn = true;
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var academyParameters =
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new MLAgents.CommunicatorObjects.UnityRLInitializationOutput();
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academyParameters.Name = gameObject.name;
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academyParameters.Version = kApiVersion;
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foreach (var brain in brains)
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{
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var bp = brain.brainParameters;
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academyParameters.BrainParameters.Add(
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MLAgents.Batcher.BrainParametersConvertor(
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bp,
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brain.gameObject.name,
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(MLAgents.CommunicatorObjects.BrainTypeProto)
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brain.brainType));
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}
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academyParameters.EnvironmentParameters =
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new MLAgents.CommunicatorObjects.EnvironmentParametersProto();
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foreach (var key in resetParameters.Keys)
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{
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academyParameters.EnvironmentParameters.FloatParameters.Add(
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key, resetParameters[key]
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);
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}
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var pythonParameters = brainBatcher.SendAcademyParameters(academyParameters);
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Random.InitState(pythonParameters.Seed);
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Application.logMessageReceived += HandleLog;
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logPath = Path.GetFullPath(".") + "/unity-environment.log";
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logWriter = new StreamWriter(logPath, false);
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logWriter.WriteLine(System.DateTime.Now.ToString());
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logWriter.WriteLine(" ");
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logWriter.Close();
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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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isInference = !isCommunicatorOn;
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BrainDecideAction += () => { };
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AgentSetStatus += (m, d, i) => { };
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AgentResetIfDone += () => { };
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AgentSendState += () => { };
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AgentAct += () => { };
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AgentForceReset += () => { };
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// Configure the environment using the configurations provided by
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// the developer in the Editor.
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SetIsInference(!brainBatcher.GetIsTraining());
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ConfigureEnvironment();
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}
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private void UpdateResetParameters()
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{
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var newResetParameters = brainBatcher.GetEnvironmentParameters();
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if (newResetParameters != null)
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{
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foreach (var kv in newResetParameters.FloatParameters)
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{
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resetParameters[kv.Key] = kv.Value;
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}
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}
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}
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void HandleLog(string logString, string stackTrace, LogType type)
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{
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logWriter = new StreamWriter(logPath, true);
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logWriter.WriteLine(type.ToString());
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logWriter.WriteLine(logString);
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logWriter.WriteLine(stackTrace);
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logWriter.Close();
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}
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/// <summary>
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/// Configures the environment settings depending on the training/inference
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/// mode and the corresponding parameters passed in the Editor.
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/// </summary>
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void ConfigureEnvironment()
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{
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if (isInference)
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{
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ConfigureEnvironmentHelper(inferenceConfiguration);
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Monitor.SetActive(true);
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}
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else
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{
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ConfigureEnvironmentHelper(trainingConfiguration);
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Monitor.SetActive(false);
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}
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}
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/// <summary>
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/// Helper method for initializing the environment based on the provided
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/// configuration.
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/// </summary>
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/// <param name="config">
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/// Environment configuration (specified in the Editor).
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/// </param>
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static void ConfigureEnvironmentHelper(EnvironmentConfiguration config)
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{
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Screen.SetResolution(config.width, config.height, false);
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QualitySettings.SetQualityLevel(config.qualityLevel, true);
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Time.timeScale = config.timeScale;
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Time.captureFramerate = 60;
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Application.targetFrameRate = config.targetFrameRate;
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}
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/// <summary>
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/// Initializes the academy and environment. Called during the waking-up
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/// phase of the environment before any of the scene objects/agents have
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/// been initialized.
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/// </summary>
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public virtual void InitializeAcademy()
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{
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}
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/// <summary>
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/// Specifies the academy behavior at every step of the environment.
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/// </summary>
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public virtual void AcademyStep()
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{
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}
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/// <summary>
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/// Specifies the academy behavior when being reset (i.e. at the completion
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/// of a global episode).
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/// </summary>
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public virtual void AcademyReset()
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{
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}
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/// <summary>
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/// Returns the <see cref="isInference"/> flag.
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/// </summary>
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/// <returns>
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/// <c>true</c>, if current mode is inference, <c>false</c> if training.
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/// </returns>
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public bool GetIsInference()
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{
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return isInference;
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}
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/// <summary>
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/// Sets the <see cref="isInference"/> flag to the provided value. If
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/// the new flag differs from the current flag value, this signals that
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/// the environment configuration needs to be updated.
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/// </summary>
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/// <param name="isInference">
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/// Environment mode, if true then inference, otherwise training.
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/// </param>
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public void SetIsInference(bool isInference)
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{
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if (this.isInference != isInference)
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{
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this.isInference = isInference;
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// This signals to the academy that at the next environment step
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// the engine configurations need updating to the respective mode
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// (i.e. training vs inference) configuraiton.
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modeSwitched = true;
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}
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}
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/// <summary>
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/// Returns the current episode counter.
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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 GetEpisodeCount()
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{
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return episodeCount;
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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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/// 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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/// Returns whether or not the academy is done.
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/// </summary>
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/// <returns>
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/// <c>true</c>, if academy 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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/// <summary>
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/// Returns whether or not the communicator is on.
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/// </summary>
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/// <returns>
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/// <c>true</c>, if communicator is on, <c>false</c> otherwise.
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/// </returns>
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public bool IsCommunicatorOn()
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{
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return isCommunicatorOn;
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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();
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firstAcademyReset = true;
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}
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/// <summary>
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/// Performs a single environment update to the Academy, Brain and Agent
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/// objects within the environment.
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/// </summary>
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void EnvironmentStep()
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{
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if (modeSwitched)
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{
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ConfigureEnvironment();
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modeSwitched = false;
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}
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if ((isCommunicatorOn) &&
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(lastCommunicatorMessageNumber != brainBatcher.GetNumberMessageReceived()))
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{
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lastCommunicatorMessageNumber = brainBatcher.GetNumberMessageReceived();
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if (brainBatcher.GetCommand() ==
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MLAgents.CommunicatorObjects.CommandProto.Reset)
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{
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UpdateResetParameters();
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SetIsInference(!brainBatcher.GetIsTraining());
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ForcedFullReset();
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}
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if (brainBatcher.GetCommand() ==
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MLAgents.CommunicatorObjects.CommandProto.Quit)
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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();
|
|
return;
|
|
}
|
|
}
|
|
else if (!firstAcademyReset)
|
|
{
|
|
UpdateResetParameters();
|
|
ForcedFullReset();
|
|
}
|
|
|
|
if ((stepCount >= maxSteps) && maxSteps > 0)
|
|
{
|
|
maxStepReached = true;
|
|
Done();
|
|
}
|
|
|
|
AgentSetStatus(maxStepReached, done, stepCount);
|
|
|
|
brainBatcher.RegisterAcademyDoneFlag(done);
|
|
|
|
if (done)
|
|
{
|
|
EnvironmentReset();
|
|
}
|
|
|
|
AgentResetIfDone();
|
|
|
|
AgentSendState();
|
|
|
|
BrainDecideAction();
|
|
|
|
AcademyStep();
|
|
|
|
AgentAct();
|
|
|
|
stepCount += 1;
|
|
}
|
|
|
|
/// <summary>
|
|
/// Resets the environment, including the Academy.
|
|
/// </summary>
|
|
void EnvironmentReset()
|
|
{
|
|
stepCount = 0;
|
|
episodeCount++;
|
|
done = false;
|
|
maxStepReached = false;
|
|
AcademyReset();
|
|
}
|
|
|
|
/// <summary>
|
|
/// Monobehavior function that dictates each environment step.
|
|
/// </summary>
|
|
void FixedUpdate()
|
|
{
|
|
EnvironmentStep();
|
|
}
|
|
|
|
/// <summary>
|
|
/// Helper method that retrieves the Brain objects that are currently
|
|
/// specified as children of the Academy within the Editor.
|
|
/// </summary>
|
|
/// <param name="academy">Academy.</param>
|
|
/// <returns>
|
|
/// List of brains currently attached to academy.
|
|
/// </returns>
|
|
static List<Brain> GetBrains(GameObject academy)
|
|
{
|
|
List<Brain> brains = new List<Brain>();
|
|
var transform = academy.transform;
|
|
|
|
for (var i = 0; i < transform.childCount; i++)
|
|
{
|
|
var child = transform.GetChild(i);
|
|
var brain = child.GetComponent<Brain>();
|
|
|
|
if (brain != null && child.gameObject.activeSelf)
|
|
{
|
|
brains.Add(brain);
|
|
}
|
|
}
|
|
|
|
return brains;
|
|
}
|
|
}
|
|
}
|