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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140 行
4.7 KiB

using System;
using System.Collections.Generic;
using Barracuda;
using MLAgents.InferenceBrain;
namespace MLAgents.Sensor
{
/// <summary>
/// Allows sensors to write to both TensorProxy and float arrays/lists.
/// </summary>
public class WriteAdapter
{
IList<float> m_Data;
int m_Offset;
TensorProxy m_Proxy;
int m_Batch;
TensorShape m_TensorShape;
/// <summary>
/// Set the adapter to write to an IList at the given channelOffset.
/// </summary>
/// <param name="data">Float array or list that will be written to.</param>
/// <param name="shape">Shape of the observations to be written.</param>
/// <param name="offset">Offset from the start of the float data to write to.</param>
public void SetTarget(IList<float> data, int[] shape, int offset)
{
m_Data = data;
m_Offset = offset;
m_Proxy = null;
m_Batch = 0;
if (shape.Length == 1)
{
m_TensorShape = new TensorShape(m_Batch, shape[0]);
}
else
{
m_TensorShape = new TensorShape(m_Batch, shape[0], shape[1], shape[2]);
}
}
/// <summary>
/// Set the adapter to write to a TensorProxy at the given batch and channel offset.
/// </summary>
/// <param name="tensorProxy">Tensor proxy that will be writtent to.</param>
/// <param name="batchIndex">Batch index in the tensor proxy (i.e. the index of the Agent)</param>
/// <param name="channelOffset">Offset from the start of the channel to write to.</param>
public void SetTarget(TensorProxy tensorProxy, int batchIndex, int channelOffset)
{
m_Proxy = tensorProxy;
m_Batch = batchIndex;
m_Offset = channelOffset;
m_Data = null;
m_TensorShape = m_Proxy.data.shape;
}
/// <summary>
/// 1D write access at a specified index. Use AddRange if possible instead.
/// </summary>
/// <param name="index">Index to write to</param>
public float this[int index]
{
set
{
if (m_Data != null)
{
m_Data[index + m_Offset] = value;
}
else
{
m_Proxy.data[m_Batch, index + m_Offset] = value;
}
}
}
/// <summary>
/// 3D write access at the specified height, width, and channel. Only usable with a TensorProxy target.
/// </summary>
/// <param name="h"></param>
/// <param name="w"></param>
/// <param name="ch"></param>
public float this[int h, int w, int ch]
{
set
{
if (m_Data != null)
{
if (h < 0 || h >= m_TensorShape.height)
{
throw new IndexOutOfRangeException($"height value {h} must be in range [0, {m_TensorShape.height - 1}]");
}
if (w < 0 || w >= m_TensorShape.width)
{
throw new IndexOutOfRangeException($"width value {w} must be in range [0, {m_TensorShape.width - 1}]");
}
if (ch < 0 || ch >= m_TensorShape.channels)
{
throw new IndexOutOfRangeException($"channel value {ch} must be in range [0, {m_TensorShape.channels - 1}]");
}
var index = m_TensorShape.Index(m_Batch, h, w, ch + m_Offset);
m_Data[index] = value;
}
else
{
m_Proxy.data[m_Batch, h, w, ch + m_Offset] = value;
}
}
}
/// <summary>
/// Write the range of floats
/// </summary>
/// <param name="data"></param>
/// <param name="writeOffset">Optional write offset</param>
public void AddRange(IEnumerable<float> data, int writeOffset = 0)
{
if (m_Data != null)
{
int index = 0;
foreach (var val in data)
{
m_Data[index + m_Offset + writeOffset] = val;
index++;
}
}
else
{
int index = 0;
foreach (var val in data)
{
m_Proxy.data[m_Batch, index + m_Offset + writeOffset] = val;
index++;
}
}
}
}
}