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.Collections.Generic;
#if UNITY_2020_1_OR_NEWER
using UnityEngine;
#endif
using Unity.MLAgents.Sensors;
namespace Unity.MLAgents.Extensions.Sensors
{
/// <summary>
/// ISensor implementation that generates observations for a group of Rigidbodies or ArticulationBodies.
/// </summary>
public class PhysicsBodySensor : ISensor
{
int[] m_Shape;
string m_SensorName;
PoseExtractor m_PoseExtractor;
List<IJointExtractor> m_JointExtractors;
PhysicsSensorSettings m_Settings;
/// <summary>
/// Construct a new PhysicsBodySensor
/// </summary>
/// <param name="poseExtractor"></param>
/// <param name="settings"></param>
/// <param name="sensorName"></param>
public PhysicsBodySensor(
RigidBodyPoseExtractor poseExtractor,
PhysicsSensorSettings settings,
string sensorName
)
{
m_PoseExtractor = poseExtractor;
m_SensorName = sensorName;
m_Settings = settings;
var numJointExtractorObservations = 0;
m_JointExtractors = new List<IJointExtractor>(poseExtractor.NumEnabledPoses);
foreach (var rb in poseExtractor.GetEnabledRigidbodies())
{
var jointExtractor = new RigidBodyJointExtractor(rb);
numJointExtractorObservations += jointExtractor.NumObservations(settings);
m_JointExtractors.Add(jointExtractor);
}
var numTransformObservations = m_PoseExtractor.GetNumPoseObservations(settings);
m_Shape = new[] { numTransformObservations + numJointExtractorObservations };
}
#if UNITY_2020_1_OR_NEWER
public PhysicsBodySensor(ArticulationBody rootBody, PhysicsSensorSettings settings, string sensorName=null)
{
var poseExtractor = new ArticulationBodyPoseExtractor(rootBody);
m_PoseExtractor = poseExtractor;
m_SensorName = string.IsNullOrEmpty(sensorName) ? $"ArticulationBodySensor:{rootBody?.name}" : sensorName;
m_Settings = settings;
var numJointExtractorObservations = 0;
m_JointExtractors = new List<IJointExtractor>(poseExtractor.NumEnabledPoses);
foreach(var articBody in poseExtractor.GetEnabledArticulationBodies())
{
var jointExtractor = new ArticulationBodyJointExtractor(articBody);
numJointExtractorObservations += jointExtractor.NumObservations(settings);
m_JointExtractors.Add(jointExtractor);
}
var numTransformObservations = m_PoseExtractor.GetNumPoseObservations(settings);
m_Shape = new[] { numTransformObservations + numJointExtractorObservations };
}
#endif
/// <inheritdoc/>
public int[] GetObservationShape()
{
return m_Shape;
}
/// <inheritdoc/>
public int Write(ObservationWriter writer)
{
var numWritten = writer.WritePoses(m_Settings, m_PoseExtractor);
foreach (var jointExtractor in m_JointExtractors)
{
numWritten += jointExtractor.Write(m_Settings, writer, numWritten);
}
return numWritten;
}
/// <inheritdoc/>
public byte[] GetCompressedObservation()
{
return null;
}
/// <inheritdoc/>
public void Update()
{
if (m_Settings.UseModelSpace)
{
m_PoseExtractor.UpdateModelSpacePoses();
}
if (m_Settings.UseLocalSpace)
{
m_PoseExtractor.UpdateLocalSpacePoses();
}
}
/// <inheritdoc/>
public void Reset() { }
/// <inheritdoc/>
public SensorCompressionType GetCompressionType()
{
return SensorCompressionType.None;
}
/// <inheritdoc/>
public string GetName()
{
return m_SensorName;
}
}
}