Unity 机器学习代理工具包 (ML-Agents) 是一个开源项目,它使游戏和模拟能够作为训练智能代理的环境。
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using System;
using UnityEngine;
using System.Collections.Generic;
#if UNITY_EDITOR
using UnityEditor;
#endif
using MLAgents.Inference;
using MLAgents.Policies;
using MLAgents.SideChannels;
using Barracuda;
/**
* Welcome to Unity Machine Learning Agents (ML-Agents).
*
* The ML-Agents toolkit contains four entities: Academy, Agent, Communicator and
* Python API. The academy and connected agents live within
* a learning environment (herein called Environment), while the communicator
* manages the communication between the learning environment and the Python
* API. For more information on each of these entities, in addition to how to
* set-up a learning environment and train the behavior of characters in a
* Unity scene, please browse our documentation pages on GitHub:
* https://github.com/Unity-Technologies/ml-agents/blob/master/docs/
*/
namespace MLAgents
{
/// <summary>
/// Helper class to step the Academy during FixedUpdate phase.
/// </summary>
internal class AcademyFixedUpdateStepper : MonoBehaviour
{
void FixedUpdate()
{
Academy.Instance.EnvironmentStep();
}
}
/// <summary>
/// An Academy is where Agent objects go to train their behaviors.
/// </summary>
/// <remarks>
/// When an academy is run, it can either be in inference or training mode.
/// The mode is determined by the presence or absence of a Communicator. In
/// the presence of a communicator, the academy is run in training mode where
/// the states and observations of each agent are sent through the
/// communicator. In the absence of a communicator, the academy is run in
/// inference mode where the agent behavior is determined by the Policy
/// attached to it.
/// </remarks>
[HelpURL("https://github.com/Unity-Technologies/ml-agents/blob/master/" +
"docs/Learning-Environment-Design.md")]
public class Academy : IDisposable
{
/// <summary>
/// Communication protocol version.
/// When connecting to python, this must match UnityEnvironment.API_VERSION.
/// Currently we require strict equality between the communication protocol
/// on each side, although we may allow some flexibility in the future.
/// This should be incremented whenever a change is made to the communication protocol.
/// </summary>
const string k_ApiVersion = "0.16.0";
/// <summary>
/// Unity package version of com.unity.ml-agents.
/// This must match the version string in package.json and is checked in a unit test.
/// </summary>
internal const string k_PackageVersion = "0.15.1-preview";
const int k_EditorTrainingPort = 5004;
const string k_portCommandLineFlag = "--mlagents-port";
// Lazy initializer pattern, see https://csharpindepth.com/articles/singleton#lazy
static Lazy<Academy> s_Lazy = new Lazy<Academy>(() => new Academy());
/// <summary>
/// True if the Academy is initialized, false otherwise.
/// </summary>
public static bool IsInitialized
{
get { return s_Lazy.IsValueCreated; }
}
/// <summary>
/// The singleton Academy object.
/// </summary>
public static Academy Instance { get { return s_Lazy.Value; } }
// Fields not provided in the Inspector.
/// <summary>
/// Returns whether or not the communicator is on.
/// </summary>
/// <returns>
/// <c>true</c>, if communicator is on, <c>false</c> otherwise.
/// </returns>
public bool IsCommunicatorOn
{
get { return Communicator != null; }
}
/// The number of episodes completed by the environment. Incremented
/// each time the environment is reset.
int m_EpisodeCount;
/// The number of steps completed within the current episode. Incremented
/// each time a step is taken in the environment. Is reset to 0 during
/// <see cref="EnvironmentReset"/>.
int m_StepCount;
/// The number of total number of steps completed during the whole simulation. Incremented
/// each time a step is taken in the environment.
int m_TotalStepCount;
/// Pointer to the communicator currently in use by the Academy.
internal ICommunicator Communicator;
bool m_Initialized;
List<ModelRunner> m_ModelRunners = new List<ModelRunner>();
// Flag used to keep track of the first time the Academy is reset.
bool m_HadFirstReset;
// The Academy uses a series of events to communicate with agents
// to facilitate synchronization. More specifically, it ensure
// that all the agents performs their steps in a consistent order (i.e. no
// agent can act based on a decision before another agent has had a chance
// to request a decision).
// Signals to all the Agents at each environment step so they can use
// their Policy to decide on their next action.
internal event Action DecideAction;
// Signals to all the listeners that the academy is being destroyed
internal event Action DestroyAction;
// Signals the Agent that a new step is about to start.
// This will mark the Agent as Done if it has reached its maxSteps.
internal event Action AgentIncrementStep;
/// <summary>
/// Signals to all of the <see cref="Agent"/>s that their step is about to begin.
/// This is a good time for an <see cref="Agent"/> to decide if it would like to
/// call <see cref="Agent.RequestDecision"/> or <see cref="Agent.RequestAction"/>
/// for this step. Any other pre-step setup could be done during this even as well.
/// </summary>
public event Action<int> AgentPreStep;
// Signals to all the agents at each environment step so they can send
// their state to their Policy if they have requested a decision.
internal event Action AgentSendState;
// Signals to all the agents at each environment step so they can act if
// they have requested a decision.
internal event Action AgentAct;
// Signals to all the agents each time the Academy force resets.
internal event Action AgentForceReset;
/// <summary>
/// Signals that the Academy has been reset by the training process.
/// </summary>
public event Action OnEnvironmentReset;
AcademyFixedUpdateStepper m_FixedUpdateStepper;
GameObject m_StepperObject;
/// <summary>
/// Private constructor called the first time the Academy is used.
/// Academy uses this time to initialize internal data
/// structures, initialize the environment and check for the existence
/// of a communicator.
/// </summary>
Academy()
{
Application.quitting += Dispose;
LazyInitialize();
}
/// <summary>
/// Initialize the Academy if it hasn't already been initialized.
/// This method is always safe to call; it will have no effect if the Academy is already
/// initialized.
/// </summary>
internal void LazyInitialize()
{
if (!m_Initialized)
{
InitializeEnvironment();
m_Initialized = true;
}
}
/// <summary>
/// Enable stepping of the Academy during the FixedUpdate phase. This is done by creating
/// a temporary GameObject with a MonoBehaviour that calls Academy.EnvironmentStep().
/// </summary>
void EnableAutomaticStepping()
{
if (m_FixedUpdateStepper != null)
{
return;
}
m_StepperObject = new GameObject("AcademyFixedUpdateStepper");
// Don't show this object in the hierarchy
m_StepperObject.hideFlags = HideFlags.HideInHierarchy;
m_FixedUpdateStepper = m_StepperObject.AddComponent<AcademyFixedUpdateStepper>();
}
/// <summary>
/// Disable stepping of the Academy during the FixedUpdate phase. If this is called, the Academy must be
/// stepped manually by the user by calling Academy.EnvironmentStep().
/// </summary>
void DisableAutomaticStepping()
{
if (m_FixedUpdateStepper == null)
{
return;
}
m_FixedUpdateStepper = null;
if (Application.isEditor)
{
UnityEngine.Object.DestroyImmediate(m_StepperObject);
}
else
{
UnityEngine.Object.Destroy(m_StepperObject);
}
m_StepperObject = null;
}
/// <summary>
/// Determines whether or not the Academy is automatically stepped during the FixedUpdate phase.
/// </summary>
public bool AutomaticSteppingEnabled
{
get { return m_FixedUpdateStepper != null; }
set
{
if (value)
{
EnableAutomaticStepping();
}
else
{
DisableAutomaticStepping();
}
}
}
// Used to read Python-provided environment parameters
static int ReadPortFromArgs()
{
var args = Environment.GetCommandLineArgs();
var inputPort = "";
for (var i = 0; i < args.Length; i++)
{
if (args[i] == k_portCommandLineFlag)
{
inputPort = args[i + 1];
}
}
try
{
return int.Parse(inputPort);
}
catch
{
// No arg passed, or malformed port number.
#if UNITY_EDITOR
// Try connecting on the default editor port
return k_EditorTrainingPort;
#else
// This is an executable, so we don't try to connect.
return -1;
#endif
}
}
/// <summary>
/// Initializes the environment, configures it and initialized the Academy.
/// </summary>
void InitializeEnvironment()
{
TimerStack.Instance.AddMetadata("communication_protocol_version", k_ApiVersion);
TimerStack.Instance.AddMetadata("package_version", k_PackageVersion);
EnableAutomaticStepping();
SideChannelUtils.RegisterSideChannel(new EngineConfigurationChannel());
SideChannelUtils.RegisterSideChannel(new FloatPropertiesChannel());
SideChannelUtils.RegisterSideChannel(new StatsSideChannel());
// Try to launch the communicator by using the arguments passed at launch
var port = ReadPortFromArgs();
if (port > 0)
{
Communicator = new RpcCommunicator(
new CommunicatorInitParameters
{
port = port
}
);
}
if (Communicator != null)
{
// We try to exchange the first message with Python. If this fails, it means
// no Python Process is ready to train the environment. In this case, the
//environment must use Inference.
try
{
var unityRlInitParameters = Communicator.Initialize(
new CommunicatorInitParameters
{
unityCommunicationVersion = k_ApiVersion,
unityPackageVersion = k_PackageVersion,
name = "AcademySingleton",
});
UnityEngine.Random.InitState(unityRlInitParameters.seed);
}
catch
{
Debug.Log($"" +
$"Couldn't connect to trainer on port {port} using API version {k_ApiVersion}. " +
"Will perform inference instead."
);
Communicator = null;
}
if (Communicator != null)
{
Communicator.QuitCommandReceived += OnQuitCommandReceived;
Communicator.ResetCommandReceived += OnResetCommand;
}
}
// If a communicator is enabled/provided, then we assume we are in
// training mode. In the absence of a communicator, we assume we are
// in inference mode.
ResetActions();
}
void ResetActions()
{
DecideAction = () => {};
DestroyAction = () => {};
AgentPreStep = i => {};
AgentSendState = () => {};
AgentAct = () => {};
AgentForceReset = () => {};
OnEnvironmentReset = () => {};
}
static void OnQuitCommandReceived()
{
#if UNITY_EDITOR
EditorApplication.isPlaying = false;
#endif
Application.Quit();
}
void OnResetCommand()
{
ForcedFullReset();
}
/// <summary>
/// Returns the current episode counter.
/// </summary>
/// <returns>
/// Current episode number.
/// </returns>
public int EpisodeCount
{
get { return m_EpisodeCount; }
}
/// <summary>
/// Returns the current step counter (within the current episode).
/// </summary>
/// <returns>
/// Current step count.
/// </returns>
public int StepCount
{
get { return m_StepCount; }
}
/// <summary>
/// Returns the total step counter.
/// </summary>
/// <returns>
/// Total step count.
/// </returns>
public int TotalStepCount
{
get { return m_TotalStepCount; }
}
/// <summary>
/// Forces the full reset. The done flags are not affected. Is either
/// called the first reset at inference and every external reset
/// at training.
/// </summary>
void ForcedFullReset()
{
EnvironmentReset();
AgentForceReset?.Invoke();
m_HadFirstReset = true;
}
/// <summary>
/// Performs a single environment update to the Academy, and Agent
/// objects within the environment.
/// </summary>
public void EnvironmentStep()
{
if (!m_HadFirstReset)
{
ForcedFullReset();
}
AgentPreStep?.Invoke(m_StepCount);
m_StepCount += 1;
m_TotalStepCount += 1;
AgentIncrementStep?.Invoke();
using (TimerStack.Instance.Scoped("AgentSendState"))
{
AgentSendState?.Invoke();
}
using (TimerStack.Instance.Scoped("DecideAction"))
{
DecideAction?.Invoke();
}
// If the communicator is not on, we need to clear the SideChannel sending queue
if (!IsCommunicatorOn)
{
SideChannelUtils.GetSideChannelMessage();
}
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="brainParameters">The brainParameters used to create 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>
internal 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>
/// 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;
SideChannelUtils.UnregisterAllSideChannels();
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());
}
}
}