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615 行
22 KiB
615 行
22 KiB
using UnityEngine;
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using System.Collections.Generic;
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using UnityEngine.Serialization;
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#if UNITY_EDITOR
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using UnityEditor;
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#endif
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using MLAgents.InferenceBrain;
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using 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/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.)")]
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[Range(0, 5)]
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public int qualityLevel;
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[Tooltip("Speed at which environment is run. (Higher is faster.)")]
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[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.
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/// 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 communicator, the academy is run in
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/// inference mode where the agent behavior is determined by the Policy
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/// attached to it.
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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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const string k_ApiVersion = "API-11";
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/// Temporary storage for global gravity value
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/// Used to restore oringal value when deriving Academy modifies it
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Vector3 m_OriginalGravity;
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/// Temporary storage for global fixedDeltaTime value
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/// Used to restore original value when deriving Academy modifies it
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float m_OriginalFixedDeltaTime;
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/// Temporary storage for global maximumDeltaTime value
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/// Used to restore original value when deriving Academy modifies it
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float m_OriginalMaximumDeltaTime;
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// Fields provided in the Inspector
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[FormerlySerializedAs("trainingConfiguration")]
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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 m_TrainingConfiguration =
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new EnvironmentConfiguration(80, 80, 1, 100.0f, -1);
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[FormerlySerializedAs("inferenceConfiguration")]
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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 m_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 by passing a 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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public CommunicatorObjects.CustomResetParametersProto customResetParameters;
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// Fields not provided in the Inspector.
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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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get { return Communicator != null; }
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}
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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 during
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/// training via <see cref="SetIsInference"/>.
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bool m_IsInference = true;
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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="AcademyReset"/>.
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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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/// Flag that indicates whether the inference/training mode of the
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/// environment was switched by the training process. This impacts the
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/// engine settings at the next environment step.
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bool m_ModeSwitched;
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/// Pointer to the communicator currently in use by the Academy.
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public 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_FirstAcademyReset;
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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 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 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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public event System.Action DecideAction;
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// Signals to all the listeners that the academy is being destroyed
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public event System.Action DestroyAction;
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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<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 Policy 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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// Signals 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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LazyInitialization();
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}
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public void LazyInitialization()
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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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// Used to read Python-provided environment parameters
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static 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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void InitializeEnvironment()
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{
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m_OriginalGravity = Physics.gravity;
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m_OriginalFixedDeltaTime = Time.fixedDeltaTime;
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m_OriginalMaximumDeltaTime = Time.maximumDeltaTime;
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InitializeAcademy();
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// Try to launch the communicator by using the arguments passed at launch
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try
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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 = ReadArgs()
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});
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}
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catch
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{
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#if UNITY_EDITOR
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Communicator = new RpcCommunicator(
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new CommunicatorInitParameters
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{
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port = 5004
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});
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#endif
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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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version = k_ApiVersion,
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name = gameObject.name,
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environmentResetParameters = new EnvironmentResetParameters
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{
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resetParameters = resetParameters,
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customResetParameters = customResetParameters
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}
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});
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Random.InitState(unityRLInitParameters.seed);
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}
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catch
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{
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Communicator = null;
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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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Communicator.RLInputReceived += OnRLInputReceived;
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}
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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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SetIsInference(!IsCommunicatorOn);
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DecideAction += () => { };
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DestroyAction += () => { };
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AgentSetStatus += 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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ConfigureEnvironment();
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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(EnvironmentResetParameters newResetParameters)
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{
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UpdateResetParameters(newResetParameters);
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ForcedFullReset();
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}
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void OnRLInputReceived(UnityRLInputParameters inputParams)
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{
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m_IsInference = !inputParams.isTraining;
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}
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void UpdateResetParameters(EnvironmentResetParameters newResetParameters)
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{
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if (newResetParameters.resetParameters != null)
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{
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foreach (var kv in newResetParameters.resetParameters)
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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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customResetParameters = newResetParameters.customResetParameters;
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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 (m_IsInference)
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{
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ConfigureEnvironmentHelper(m_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(m_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="m_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 m_IsInference;
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}
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/// <summary>
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/// Sets the <see cref="m_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 (m_IsInference != isInference)
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{
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m_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) configuration.
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m_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 m_EpisodeCount;
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}
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/// <summary>
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/// Returns the current step counter (within the current episode).
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/// </summary>
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/// <returns>
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/// Current step count.
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/// </returns>
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public int GetStepCount()
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{
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return m_StepCount;
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}
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/// <summary>
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/// Returns the total step counter.
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/// </summary>
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/// <returns>
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/// Total step count.
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/// </returns>
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public int GetTotalStepCount()
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{
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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_FirstAcademyReset = true;
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}
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/// <summary>
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/// Performs a single environment update to the Academy, 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 (m_ModeSwitched)
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{
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ConfigureEnvironment();
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m_ModeSwitched = false;
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}
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if (!m_FirstAcademyReset)
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{
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ForcedFullReset();
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}
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AgentSetStatus?.Invoke(m_StepCount);
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using (TimerStack.Instance.Scoped("AgentResetIfDone"))
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{
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AgentResetIfDone?.Invoke();
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}
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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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using (TimerStack.Instance.Scoped("AcademyStep"))
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{
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AcademyStep();
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}
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using (TimerStack.Instance.Scoped("AgentAct"))
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{
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AgentAct?.Invoke();
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}
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m_StepCount += 1;
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m_TotalStepCount += 1;
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}
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/// <summary>
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/// Resets the environment, including the Academy.
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/// </summary>
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void EnvironmentReset()
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{
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m_StepCount = 0;
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m_EpisodeCount++;
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AcademyReset();
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}
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/// <summary>
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/// MonoBehaviour function that dictates each environment step.
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/// </summary>
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void FixedUpdate()
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{
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EnvironmentStep();
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}
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/// <summary>
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/// Creates or retrieves an existing ModelRunner that uses the same
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/// NNModel and the InferenceDevice as provided.
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/// </summary>
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/// <param name="model"> The NNModel the ModelRunner must use </param>
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/// <param name="brainParameters"> The brainParameters used to create
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|
/// the ModelRunner </param>
|
|
/// <param name="inferenceDevice"> The inference device (CPU or GPU)
|
|
/// the ModelRunner will use </param>
|
|
/// <returns> The ModelRunner compatible with the input settings</returns>
|
|
public ModelRunner GetOrCreateModelRunner(
|
|
NNModel model, BrainParameters brainParameters, InferenceDevice inferenceDevice)
|
|
{
|
|
var modelRunner = m_ModelRunners.Find(x => x.HasModel(model, inferenceDevice));
|
|
if (modelRunner == null)
|
|
{
|
|
modelRunner = new ModelRunner(
|
|
model, brainParameters, inferenceDevice);
|
|
m_ModelRunners.Add(modelRunner);
|
|
}
|
|
return modelRunner;
|
|
}
|
|
|
|
/// <summary>
|
|
/// Cleanup function
|
|
/// </summary>
|
|
protected virtual void OnDestroy()
|
|
{
|
|
Physics.gravity = m_OriginalGravity;
|
|
Time.fixedDeltaTime = m_OriginalFixedDeltaTime;
|
|
Time.maximumDeltaTime = m_OriginalMaximumDeltaTime;
|
|
|
|
// Signal to listeners that the academy is being destroyed now
|
|
DestroyAction?.Invoke();
|
|
|
|
foreach (var mr in m_ModelRunners)
|
|
{
|
|
mr.Dispose();
|
|
}
|
|
|
|
// TODO - Pass worker ID or some other identifier,
|
|
// so that multiple envs won't overwrite each others stats.
|
|
TimerStack.Instance.SaveJsonTimers();
|
|
}
|
|
}
|
|
}
|