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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3.2 KiB

using System.Collections.Generic;
using Unity.Barracuda;
using NUnit.Framework;
using Unity.MLAgents.Actuators;
using Unity.MLAgents.Inference;
namespace Unity.MLAgents.Tests
{
public class DiscreteActionOutputApplierTest
{
[Test]
public void TestDiscreteApply()
{
var actionSpec = ActionSpec.MakeDiscrete(3, 2);
var applier = new DiscreteActionOutputApplier(actionSpec, 2020, null);
var agentIds = new List<int> { 42, 1337 };
var actionBuffers = new Dictionary<int, ActionBuffers>();
actionBuffers[42] = new ActionBuffers(actionSpec);
actionBuffers[1337] = new ActionBuffers(actionSpec);
var actionTensor = new TensorProxy
{
data = new Tensor(
2,
2,
new[]
{
2.0f, // Agent 0, branch 0
1.0f, // Agent 0, branch 1
0.0f, // Agent 1, branch 0
0.0f // Agent 1, branch 1
}),
shape = new long[] { 2, 2 },
valueType = TensorProxy.TensorType.FloatingPoint
};
applier.Apply(actionTensor, agentIds, actionBuffers);
Assert.AreEqual(2, actionBuffers[42].DiscreteActions[0]);
Assert.AreEqual(1, actionBuffers[42].DiscreteActions[1]);
Assert.AreEqual(0, actionBuffers[1337].DiscreteActions[0]);
Assert.AreEqual(0, actionBuffers[1337].DiscreteActions[1]);
}
}
public class LegacyDiscreteActionOutputApplierTest
{
[Test]
public void TestDiscreteApply()
{
var actionSpec = ActionSpec.MakeDiscrete(3, 2);
const float smallLogProb = -1000.0f;
const float largeLogProb = -1.0f;
var logProbs = new TensorProxy
{
data = new Tensor(
2,
5,
new[]
{
smallLogProb, smallLogProb, largeLogProb, // Agent 0, branch 0
smallLogProb, largeLogProb, // Agent 0, branch 1
largeLogProb, smallLogProb, smallLogProb, // Agent 1, branch 0
largeLogProb, smallLogProb, // Agent 1, branch 1
}),
valueType = TensorProxy.TensorType.FloatingPoint
};
var applier = new LegacyDiscreteActionOutputApplier(actionSpec, 2020, null);
var agentIds = new List<int> { 42, 1337 };
var actionBuffers = new Dictionary<int, ActionBuffers>();
actionBuffers[42] = new ActionBuffers(actionSpec);
actionBuffers[1337] = new ActionBuffers(actionSpec);
applier.Apply(logProbs, agentIds, actionBuffers);
Assert.AreEqual(2, actionBuffers[42].DiscreteActions[0]);
Assert.AreEqual(1, actionBuffers[42].DiscreteActions[1]);
Assert.AreEqual(0, actionBuffers[1337].DiscreteActions[0]);
Assert.AreEqual(0, actionBuffers[1337].DiscreteActions[1]);
}
}
}