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124 行
4.8 KiB
124 行
4.8 KiB
using UnityEngine;
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using System.Linq;
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namespace MLAgents
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{
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public enum SpaceType
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{
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discrete,
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continuous
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};
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/// <summary>
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/// The resolution of a camera used by an agent.
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/// The width defines the number of pixels on the horizontal axis.
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/// The height defines the number of pixels on the verical axis.
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/// blackAndWhite defines whether or not the image is grayscale.
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/// </summary>
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[System.Serializable]
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public struct Resolution
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{
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public int width;
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/**< \brief The width of the observation in pixels */
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public int height;
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/**< \brief The height of the observation in pixels */
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public bool blackAndWhite;
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/**< \brief If true, the image will be in black and white.
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* If false, it will be in colors RGB */
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}
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/// <summary>
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/// Holds information about the Brain. It defines what are the inputs and outputs of the
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/// decision process.
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/// </summary>
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[System.Serializable]
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public class BrainParameters
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{
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public int vectorObservationSize = 1;
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/**< \brief If continuous : The length of the float vector that represents
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* the state
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* <br> If discrete : The number of possible values the state can take*/
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[Range(1, 50)] public int numStackedVectorObservations = 1;
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public int[] vectorActionSize = new int[1]{1};
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/**< \brief If continuous : The length of the float vector that represents
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* the action
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* <br> If discrete : The number of possible values the action can take*/
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public Resolution[] cameraResolutions;
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/**<\brief The list of observation resolutions for the brain */
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public string[] vectorActionDescriptions;
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/**< \brief The list of strings describing what the actions correpond to */
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public SpaceType vectorActionSpaceType = SpaceType.discrete;
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/**< \brief Defines if the action is discrete or continuous */
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/// <summary>
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/// Converts a Brain into to a Protobuff BrainInfoProto so it can be sent
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/// </summary>
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/// <returns>The BrainInfoProto generated.</returns>
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/// <param name="name">The name of the brain.</param>
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/// <param name="isTraining">Whether or not the Brain is training.</param>
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public CommunicatorObjects.BrainParametersProto
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ToProto(string name, bool isTraining)
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{
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var brainParametersProto = new CommunicatorObjects.BrainParametersProto
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{
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VectorObservationSize = vectorObservationSize,
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NumStackedVectorObservations = numStackedVectorObservations,
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VectorActionSize = {vectorActionSize},
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VectorActionSpaceType =
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(CommunicatorObjects.SpaceTypeProto)vectorActionSpaceType,
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BrainName = name,
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IsTraining = isTraining
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};
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brainParametersProto.VectorActionDescriptions.AddRange(vectorActionDescriptions);
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foreach (Resolution res in cameraResolutions)
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{
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brainParametersProto.CameraResolutions.Add(
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new CommunicatorObjects.ResolutionProto
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{
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Width = res.width,
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Height = res.height,
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GrayScale = res.blackAndWhite
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});
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}
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return brainParametersProto;
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}
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public BrainParameters()
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{
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}
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public BrainParameters(CommunicatorObjects.BrainParametersProto brainParametersProto)
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{
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vectorObservationSize = brainParametersProto.VectorObservationSize;
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numStackedVectorObservations = brainParametersProto.NumStackedVectorObservations;
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vectorActionSize = brainParametersProto.VectorActionSize.ToArray();
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vectorActionDescriptions = brainParametersProto.VectorActionDescriptions.ToArray();
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vectorActionSpaceType = (SpaceType)brainParametersProto.VectorActionSpaceType;
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}
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/// <summary>
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/// Deep clones the BrainParameter object
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/// </summary>
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/// <returns> A new BrainParameter object with the same values as the original.</returns>
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public BrainParameters Clone()
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{
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return new BrainParameters()
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{
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vectorObservationSize = this.vectorObservationSize,
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numStackedVectorObservations = this.numStackedVectorObservations,
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vectorActionSize = (int[]) this.vectorActionSize.Clone(),
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cameraResolutions = (Resolution[]) this.cameraResolutions.Clone(),
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vectorActionDescriptions = (string[]) this.vectorActionDescriptions.Clone(),
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vectorActionSpaceType = this.vectorActionSpaceType
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};
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}
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}
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}
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