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.Linq;
namespace MLAgents
{
public enum SpaceType
{
Discrete,
Continuous
};
/// <summary>
/// The resolution of a camera used by an agent.
/// The width defines the number of pixels on the horizontal axis.
/// The height defines the number of pixels on the verical axis.
/// blackAndWhite defines whether or not the image is grayscale.
/// </summary>
[Serializable]
public struct Resolution
{
/// <summary>The width of the observation in pixels </summary>
public int width;
/// <summary>The height of the observation in pixels</summary>
public int height;
/// <summary>
/// If true, the image will be in black and white.
/// If false, it will be in colors RGB
/// </summary>
public bool blackAndWhite;
}
/// <summary>
/// Holds information about the Brain. It defines what are the inputs and outputs of the
/// decision process.
/// </summary>
[Serializable]
public class BrainParameters
{
/// <summary>
/// If continuous : The length of the float vector that represents
/// the state
/// If discrete : The number of possible values the state can take
/// </summary>
public int vectorObservationSize = 1;
[Range(1, 50)] public int numStackedVectorObservations = 1;
/// <summary>
/// If continuous : The length of the float vector that represents
/// the action
/// If discrete : The number of possible values the action can take*/
/// </summary>
public int[] vectorActionSize = new[] {1};
/// <summary> The list of observation resolutions for the brain</summary>
public Resolution[] cameraResolutions;
/// <summary></summary>The list of strings describing what the actions correpond to */
public string[] vectorActionDescriptions;
/// <summary>Defines if the action is discrete or continuous</summary>
public SpaceType vectorActionSpaceType = SpaceType.Discrete;
public BrainParameters()
{
}
/// <summary>
/// Converts Resolution protobuf array to C# Resolution array.
/// </summary>
private static Resolution[] ResolutionProtoToNative(
CommunicatorObjects.ResolutionProto[] resolutionProtos)
{
var localCameraResolutions = new Resolution[resolutionProtos.Length];
for (var i = 0; i < resolutionProtos.Length; i++)
{
localCameraResolutions[i] = new Resolution
{
height = resolutionProtos[i].Height,
width = resolutionProtos[i].Width,
blackAndWhite = resolutionProtos[i].GrayScale
};
}
return localCameraResolutions;
}
public BrainParameters(CommunicatorObjects.BrainParametersProto brainParametersProto)
{
vectorObservationSize = brainParametersProto.VectorObservationSize;
cameraResolutions = ResolutionProtoToNative(
brainParametersProto.CameraResolutions.ToArray()
);
numStackedVectorObservations = brainParametersProto.NumStackedVectorObservations;
vectorActionSize = brainParametersProto.VectorActionSize.ToArray();
vectorActionDescriptions = brainParametersProto.VectorActionDescriptions.ToArray();
vectorActionSpaceType = (SpaceType)brainParametersProto.VectorActionSpaceType;
}
/// <summary>
/// Deep clones the BrainParameter object
/// </summary>
/// <returns> A new BrainParameter object with the same values as the original.</returns>
public BrainParameters Clone()
{
return new BrainParameters()
{
vectorObservationSize = vectorObservationSize,
numStackedVectorObservations = numStackedVectorObservations,
vectorActionSize = (int[])vectorActionSize.Clone(),
cameraResolutions = (Resolution[])cameraResolutions.Clone(),
vectorActionDescriptions = (string[])vectorActionDescriptions.Clone(),
vectorActionSpaceType = vectorActionSpaceType
};
}
}
}