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 NUnit.Framework;
using UnityEngine;
using MLAgents.Sensor;
namespace MLAgents.Tests
{
public class Float2DSensor : ISensor
{
public int Width { get; }
public int Height { get; }
string m_Name;
int[] m_Shape;
public float[,] floatData;
public Float2DSensor(int width, int height, string name)
{
Width = width;
Height = height;
m_Name = name;
m_Shape = new[] { height, width, 1 };
floatData = new float[Height, Width];
}
public Float2DSensor(float[,] floatData, string name)
{
this.floatData = floatData;
Height = floatData.GetLength(0);
Width = floatData.GetLength(1);
m_Name = name;
m_Shape = new[] { Height, Width, 1 };
}
public string GetName()
{
return m_Name;
}
public int[] GetObservationShape()
{
return m_Shape;
}
public byte[] GetCompressedObservation()
{
return null;
}
public int Write(WriteAdapter adapter)
{
using (TimerStack.Instance.Scoped("Float2DSensor.Write"))
{
for (var h = 0; h < Height; h++)
{
for (var w = 0; w < Width; w++)
{
adapter[h, w, 0] = floatData[h, w];
}
}
var numWritten = Height * Width;
return numWritten;
}
}
public void Update() {}
public SensorCompressionType GetCompressionType()
{
return SensorCompressionType.None;
}
}
public class FloatVisualSensorTests
{
[Test]
public void TestFloat2DSensorWrite()
{
var sensor = new Float2DSensor(3, 4, "floatsensor");
for (var h = 0; h < 4; h++)
{
for (var w = 0; w < 3; w++)
{
sensor.floatData[h, w] = 3 * h + w;
}
}
var output = new float[12];
var writer = new WriteAdapter();
writer.SetTarget(output, sensor.GetObservationShape(), 0);
sensor.Write(writer);
for (var i = 0; i < 9; i++)
{
Assert.AreEqual(i, output[i]);
}
}
[Test]
public void TestFloat2DSensorExternalData()
{
var data = new float[4, 3];
var sensor = new Float2DSensor(data, "floatsensor");
Assert.AreEqual(sensor.Height, 4);
Assert.AreEqual(sensor.Width, 3);
}
}
}