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 System.Collections.Generic;
using System.Linq;
using Barracuda;
using NUnit.Framework;
using UnityEngine;
using MLAgents.InferenceBrain;
namespace MLAgents.Tests
{
public class EditModeTestInternalBrainTensorGenerator
{
private class TestAgent : Agent
{
}
private static IEnumerable<Agent> GetFakeAgentInfos()
{
var goA = new GameObject("goA");
var agentA = goA.AddComponent<TestAgent>();
var infoA = new AgentInfo
{
stackedVectorObservation = new[] {1f, 2f, 3f}.ToList(),
memories = null,
storedVectorActions = new[] {1f, 2f},
actionMasks = null
};
var goB = new GameObject("goB");
var agentB = goB.AddComponent<TestAgent>();
var infoB = new AgentInfo
{
stackedVectorObservation = new[] {4f, 5f, 6f}.ToList(),
memories = new[] {1f, 1f, 1f}.ToList(),
storedVectorActions = new[] {3f, 4f},
actionMasks = new[] {true, false, false, false, false},
};
agentA.Info = infoA;
agentB.Info = infoB;
return new List<Agent> {agentA, agentB};
}
[Test]
public void Construction()
{
var bp = new BrainParameters();
var alloc = new TensorCachingAllocator();
var tensorGenerator = new TensorGenerator(bp, 0, alloc);
Assert.IsNotNull(tensorGenerator);
alloc.Dispose();
}
[Test]
public void GenerateBatchSize()
{
var inputTensor = new TensorProxy();
var alloc = new TensorCachingAllocator();
const int batchSize = 4;
var generator = new BatchSizeGenerator(alloc);
generator.Generate(inputTensor, batchSize, null);
Assert.IsNotNull(inputTensor.data);
Assert.AreEqual(inputTensor.data[0], batchSize);
alloc.Dispose();
}
[Test]
public void GenerateSequenceLength()
{
var inputTensor = new TensorProxy();
var alloc = new TensorCachingAllocator();
const int batchSize = 4;
var generator = new SequenceLengthGenerator(alloc);
generator.Generate(inputTensor, batchSize, null);
Assert.IsNotNull(inputTensor.data);
Assert.AreEqual(inputTensor.data[0], 1);
alloc.Dispose();
}
[Test]
public void GenerateVectorObservation()
{
var inputTensor = new TensorProxy
{
shape = new long[] {2, 3}
};
const int batchSize = 4;
var agentInfos = GetFakeAgentInfos();
var alloc = new TensorCachingAllocator();
var generator = new VectorObservationGenerator(alloc);
generator.Generate(inputTensor, batchSize, agentInfos);
Assert.IsNotNull(inputTensor.data);
Assert.AreEqual(inputTensor.data[0, 0], 1);
Assert.AreEqual(inputTensor.data[0, 2], 3);
Assert.AreEqual(inputTensor.data[1, 0], 4);
Assert.AreEqual(inputTensor.data[1, 2], 6);
alloc.Dispose();
}
[Test]
public void GenerateRecurrentInput()
{
var inputTensor = new TensorProxy
{
shape = new long[] {2, 5}
};
const int batchSize = 4;
var agentInfos = GetFakeAgentInfos();
var alloc = new TensorCachingAllocator();
var generator = new RecurrentInputGenerator(alloc);
generator.Generate(inputTensor, batchSize, agentInfos);
Assert.IsNotNull(inputTensor.data);
Assert.AreEqual(inputTensor.data[0, 0], 0);
Assert.AreEqual(inputTensor.data[0, 4], 0);
Assert.AreEqual(inputTensor.data[1, 0], 1);
Assert.AreEqual(inputTensor.data[1, 4], 0);
alloc.Dispose();
}
[Test]
public void GeneratePreviousActionInput()
{
var inputTensor = new TensorProxy
{
shape = new long[] {2, 2},
valueType = TensorProxy.TensorType.Integer
};
const int batchSize = 4;
var agentInfos = GetFakeAgentInfos();
var alloc = new TensorCachingAllocator();
var generator = new PreviousActionInputGenerator(alloc);
generator.Generate(inputTensor, batchSize, agentInfos);
Assert.IsNotNull(inputTensor.data);
Assert.AreEqual(inputTensor.data[0, 0], 1);
Assert.AreEqual(inputTensor.data[0, 1], 2);
Assert.AreEqual(inputTensor.data[1, 0], 3);
Assert.AreEqual(inputTensor.data[1, 1], 4);
alloc.Dispose();
}
[Test]
public void GenerateActionMaskInput()
{
var inputTensor = new TensorProxy
{
shape = new long[] {2, 5},
valueType = TensorProxy.TensorType.FloatingPoint
};
const int batchSize = 4;
var agentInfos = GetFakeAgentInfos();
var alloc = new TensorCachingAllocator();
var generator = new ActionMaskInputGenerator(alloc);
generator.Generate(inputTensor, batchSize, agentInfos);
Assert.IsNotNull(inputTensor.data);
Assert.AreEqual(inputTensor.data[0, 0], 1);
Assert.AreEqual(inputTensor.data[0, 4], 1);
Assert.AreEqual(inputTensor.data[1, 0], 0);
Assert.AreEqual(inputTensor.data[1, 4], 1);
alloc.Dispose();
}
}
}