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 Unity.Barracuda;
using System.Collections.Generic;
using System.Diagnostics;
using Unity.MLAgents.Actuators;
using Unity.MLAgents.Inference;
using Unity.MLAgents.Sensors;
namespace Unity.MLAgents.Policies
{
/// <summary>
/// Where to perform inference.
/// </summary>
public enum InferenceDevice
{
/// <summary>
/// Default inference. This is currently the same as Burst, but may change in the future.
/// </summary>
Default = 0,
/// <summary>
/// GPU inference. Corresponds to WorkerFactory.Type.ComputePrecompiled in Barracuda.
/// </summary>
GPU = 1,
/// <summary>
/// CPU inference using Burst. Corresponds to WorkerFactory.Type.CSharpBurst in Barracuda.
/// </summary>
Burst = 2,
/// <summary>
/// CPU inference. Corresponds to in WorkerFactory.Type.CSharp Barracuda.
/// Burst is recommended instead; this is kept for legacy compatibility.
/// </summary>
CPU = 3,
}
/// <summary>
/// The Barracuda Policy uses a Barracuda Model to make decisions at
/// every step. It uses a ModelRunner that is shared across all
/// Barracuda Policies that use the same model and inference devices.
/// </summary>
internal class BarracudaPolicy : IPolicy
{
protected ModelRunner m_ModelRunner;
ActionBuffers m_LastActionBuffer;
int m_AgentId;
/// <summary>
/// Sensor shapes for the associated Agents. All Agents must have the same shapes for their Sensors.
/// </summary>
List<int[]> m_SensorShapes;
ActionSpec m_ActionSpec;
private string m_BehaviorName;
/// <summary>
/// List of actuators, only used for analytics
/// </summary>
private IList<IActuator> m_Actuators;
/// <summary>
/// Whether or not we've tried to send analytics for this model. We only ever try to send once per policy,
/// and do additional deduplication in the analytics code.
/// </summary>
private bool m_AnalyticsSent;
/// <summary>
/// Instantiate a BarracudaPolicy with the necessary objects for it to run.
/// </summary>
/// <param name="actionSpec">The action spec of the behavior.</param>
/// <param name="actuators">The actuators used for this behavior.</param>
/// <param name="model">The Neural Network to use.</param>
/// <param name="inferenceDevice">Which device Barracuda will run on.</param>
/// <param name="behaviorName">The name of the behavior.</param>
public BarracudaPolicy(
ActionSpec actionSpec,
IList<IActuator> actuators,
NNModel model,
InferenceDevice inferenceDevice,
string behaviorName
)
{
var modelRunner = Academy.Instance.GetOrCreateModelRunner(model, actionSpec, inferenceDevice);
m_ModelRunner = modelRunner;
m_BehaviorName = behaviorName;
m_ActionSpec = actionSpec;
m_Actuators = actuators;
}
/// <inheritdoc />
public void RequestDecision(AgentInfo info, List<ISensor> sensors)
{
SendAnalytics(sensors);
m_AgentId = info.episodeId;
m_ModelRunner?.PutObservations(info, sensors);
}
[Conditional("MLA_UNITY_ANALYTICS_MODULE")]
void SendAnalytics(IList<ISensor> sensors)
{
if (!m_AnalyticsSent)
{
m_AnalyticsSent = true;
Analytics.InferenceAnalytics.InferenceModelSet(
m_ModelRunner.Model,
m_BehaviorName,
m_ModelRunner.InferenceDevice,
sensors,
m_ActionSpec,
m_Actuators
);
}
}
/// <inheritdoc />
public ref readonly ActionBuffers DecideAction()
{
if (m_ModelRunner == null)
{
m_LastActionBuffer = ActionBuffers.Empty;
}
else
{
m_ModelRunner?.DecideBatch();
m_LastActionBuffer = m_ModelRunner.GetAction(m_AgentId);
}
return ref m_LastActionBuffer;
}
public void Dispose()
{
}
}
}