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 System.Collections.Generic;
namespace Unity.MLAgents.Sensors
{
/// <summary>
/// The Dimension property flags of the observations
/// </summary>
[Flags]
public enum DimensionProperty
{
/// <summary>
/// No properties specified.
/// </summary>
Unspecified = 0,
/// <summary>
/// No Property of the observation in that dimension. Observation can be processed with
/// fully connected networks.
/// </summary>
None = 1,
/// <summary>
/// Means it is suitable to do a convolution in this dimension.
/// </summary>
TranslationalEquivariance = 2,
/// <summary>
/// Means that there can be a variable number of observations in this dimension.
/// The observations are unordered.
/// </summary>
VariableSize = 4,
}
/// <summary>
/// The ObservationType enum of the Sensor.
/// </summary>
public enum ObservationType
{
/// <summary>
/// Collected observations are generic.
/// </summary>
Default = 0,
/// <summary>
/// Collected observations contain goal information.
/// </summary>
GoalSignal = 1,
}
/// <summary>
/// Sensor interface for generating observations.
/// </summary>
public interface ISensor
{
/// <summary>
/// Returns a description of the observations that will be generated by the sensor.
/// See <see cref="ObservationSpec"/> for more details, and helper methods to create one.
/// </summary>
/// <returns></returns>
ObservationSpec GetObservationSpec();
/// <summary>
/// Write the observation data directly to the <see cref="ObservationWriter"/>.
/// Note that this (and <see cref="GetCompressedObservation"/>) may
/// be called multiple times per agent step, so should not mutate any internal state.
/// </summary>
/// <param name="writer">Where the observations will be written to.</param>
/// <returns>The number of elements written.</returns>
int Write(ObservationWriter writer);
/// <summary>
/// Return a compressed representation of the observation. For small observations,
/// this should generally not be implemented. However, compressing large observations
/// (such as visual results) can significantly improve model training time.
/// </summary>
/// <returns>Compressed observation.</returns>
byte[] GetCompressedObservation();
/// <summary>
/// Update any internal state of the sensor. This is called once per each agent step.
/// </summary>
void Update();
/// <summary>
/// Resets the internal state of the sensor. This is called at the end of an Agent's episode.
/// Most implementations can leave this empty.
/// </summary>
void Reset();
/// <summary>
/// Return information on the compression type being used. If no compression is used, return
/// <see cref="CompressionSpec.Default()"/>.
/// </summary>
/// <returns>CompressionSpec used by the sensor.</returns>
CompressionSpec GetCompressionSpec();
/// <summary>
/// Get the name of the sensor. This is used to ensure deterministic sorting of the sensors
/// on an Agent, so the naming must be consistent across all sensors and agents.
/// </summary>
/// <returns>The name of the sensor.</returns>
string GetName();
}
/// <summary>
/// Helper methods to be shared by all classes that implement <see cref="ISensor"/>.
/// </summary>
public static class SensorExtensions
{
/// <summary>
/// Get the total number of elements in the ISensor's observation (i.e. the product of the
/// shape elements).
/// </summary>
/// <param name="sensor"></param>
/// <returns></returns>
public static int ObservationSize(this ISensor sensor)
{
var obsSpec = sensor.GetObservationSpec();
var count = 1;
for (var i = 0; i < obsSpec.Rank; i++)
{
count *= obsSpec.Shape[i];
}
return count;
}
}
internal static class SensorUtils
{
internal static void SortSensors(List<ISensor> sensors)
{
// Use InvariantCulture to ensure consistent sorting between different culture settings.
sensors.Sort((x, y) => string.Compare(x.GetName(), y.GetName(), StringComparison.InvariantCulture));
}
}
}