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83 行
2.8 KiB
83 行
2.8 KiB
using System;
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using UnityEngine;
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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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[Serializable]
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public struct Resolution
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{
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/// <summary>The width of the observation in pixels </summary>
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public int width;
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/// <summary>The height of the observation in pixels</summary>
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public int height;
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/// <summary>
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/// 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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/// </summary>
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public bool blackAndWhite;
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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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[Serializable]
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public class BrainParameters
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{
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/// <summary>
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/// If continuous : The length of the float vector that represents
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/// the state
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/// If discrete : The number of possible values the state can take
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/// </summary>
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public int vectorObservationSize = 1;
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[Range(1, 50)] public int numStackedVectorObservations = 1;
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/// <summary>
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/// If continuous : The length of the float vector that represents
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/// the action
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/// If discrete : The number of possible values the action can take*/
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/// </summary>
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public int[] vectorActionSize = new[] {1};
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/// <summary> The list of observation resolutions for the brain</summary>
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public Resolution[] cameraResolutions;
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/// <summary></summary>The list of strings describing what the actions correpond to */
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public string[] vectorActionDescriptions;
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/// <summary>Defines if the action is discrete or continuous</summary>
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public SpaceType vectorActionSpaceType = SpaceType.Discrete;
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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 = vectorObservationSize,
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numStackedVectorObservations = numStackedVectorObservations,
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vectorActionSize = (int[])vectorActionSize.Clone(),
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cameraResolutions = (Resolution[])cameraResolutions.Clone(),
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vectorActionDescriptions = (string[])vectorActionDescriptions.Clone(),
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vectorActionSpaceType = vectorActionSpaceType
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};
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}
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}
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}
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