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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82 行
2.5 KiB

using System.Collections.Generic;
using NUnit.Framework;
using Unity.Barracuda;
using MLAgents.Inference;
using MLAgents.Policies;
namespace MLAgents.Tests
{
public class EditModeTestInternalBrainTensorApplier
{
class TestAgent : Agent
{
}
[Test]
public void Construction()
{
var bp = new BrainParameters();
var alloc = new TensorCachingAllocator();
var mem = new Dictionary<int, List<float>>();
var tensorGenerator = new TensorApplier(bp, 0, alloc, mem);
Assert.IsNotNull(tensorGenerator);
alloc.Dispose();
}
[Test]
public void ApplyContinuousActionOutput()
{
var inputTensor = new TensorProxy()
{
shape = new long[] { 2, 3 },
data = new Tensor(2, 3, new float[] { 1, 2, 3, 4, 5, 6 })
};
var applier = new ContinuousActionOutputApplier();
var agentIds = new List<int>() { 0, 1 };
// Dictionary from AgentId to Action
var actionDict = new Dictionary<int, float[]>() { { 0, null }, { 1, null } };
applier.Apply(inputTensor, agentIds, actionDict);
Assert.AreEqual(actionDict[0][0], 1);
Assert.AreEqual(actionDict[0][1], 2);
Assert.AreEqual(actionDict[0][2], 3);
Assert.AreEqual(actionDict[1][0], 4);
Assert.AreEqual(actionDict[1][1], 5);
Assert.AreEqual(actionDict[1][2], 6);
}
[Test]
public void ApplyDiscreteActionOutput()
{
var inputTensor = new TensorProxy()
{
shape = new long[] { 2, 5 },
data = new Tensor(
2,
5,
new[] { 0.5f, 22.5f, 0.1f, 5f, 1f, 4f, 5f, 6f, 7f, 8f })
};
var alloc = new TensorCachingAllocator();
var applier = new DiscreteActionOutputApplier(new[] { 2, 3 }, 0, alloc);
var agentIds = new List<int>() { 0, 1 };
// Dictionary from AgentId to Action
var actionDict = new Dictionary<int, float[]>() { { 0, null }, { 1, null } };
applier.Apply(inputTensor, agentIds, actionDict);
Assert.AreEqual(actionDict[0][0], 1);
Assert.AreEqual(actionDict[0][1], 1);
Assert.AreEqual(actionDict[1][0], 1);
Assert.AreEqual(actionDict[1][1], 2);
alloc.Dispose();
}
}
}