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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2.5 KiB

using System;
using System.Collections.Generic;
using Unity.MLAgents.Inference.Utils;
using Random = System.Random;
namespace Unity.MLAgents
{
/// <summary>
/// Takes a list of floats that encode a sampling distribution and returns the sampling function.
/// </summary>
internal static class SamplerFactory
{
public static Func<float> CreateUniformSampler(float min, float max, int seed)
{
Random distr = new Random(seed);
return () => min + (float)distr.NextDouble() * (max - min);
}
public static Func<float> CreateGaussianSampler(float mean, float stddev, int seed)
{
RandomNormal distr = new RandomNormal(seed, mean, stddev);
return () => (float)distr.NextDouble();
}
public static Func<float> CreateMultiRangeUniformSampler(IList<float> intervals, int seed)
{
//RNG
Random distr = new Random(seed);
// Will be used to normalize intervalFuncs
float sumIntervalSizes = 0;
//The number of intervals
int numIntervals = (intervals.Count / 2);
// List that will store interval lengths
float[] intervalSizes = new float[numIntervals];
// List that will store uniform distributions
IList<Func<float>> intervalFuncs = new Func<float>[numIntervals];
// Collect all intervals and store as uniform distrus
// Collect all interval sizes
for (int i = 0; i < numIntervals; i++)
{
var min = intervals[2 * i];
var max = intervals[2 * i + 1];
var intervalSize = max - min;
sumIntervalSizes += intervalSize;
intervalSizes[i] = intervalSize;
intervalFuncs[i] = () => min + (float)distr.NextDouble() * intervalSize;
}
// Normalize interval lengths
for (int i = 0; i < numIntervals; i++)
{
intervalSizes[i] = intervalSizes[i] / sumIntervalSizes;
}
// Build cmf for intervals
for (int i = 1; i < numIntervals; i++)
{
intervalSizes[i] += intervalSizes[i - 1];
}
Multinomial intervalDistr = new Multinomial(seed + 1);
float MultiRange()
{
int sampledInterval = intervalDistr.Sample(intervalSizes);
return intervalFuncs[sampledInterval].Invoke();
}
return MultiRange;
}
}
}