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 NUnit.Framework;
using UnityEngine;
using System.Reflection;
using Barracuda;
using MLAgents.InferenceBrain;
namespace MLAgents.Tests
{
public class EditModeTestInternalBrainTensorApplier
{
class TestAgent : Agent
{
public AgentAction GetAction()
{
var f = typeof(Agent).GetField(
"m_Action", BindingFlags.Instance | BindingFlags.NonPublic);
return (AgentAction)f.GetValue(this);
}
}
List<Agent> GetFakeAgentInfos()
{
var goA = new GameObject("goA");
var agentA = goA.AddComponent<TestAgent>();
var goB = new GameObject("goB");
var agentB = goB.AddComponent<TestAgent>();
return new List<Agent> { agentA, agentB };
}
[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 agentInfos = GetFakeAgentInfos();
var applier = new ContinuousActionOutputApplier();
applier.Apply(inputTensor, agentInfos);
var agents = agentInfos;
var agent = agents[0] as TestAgent;
Assert.NotNull(agent);
var action = agent.GetAction();
Assert.AreEqual(action.vectorActions[0], 1);
Assert.AreEqual(action.vectorActions[1], 2);
Assert.AreEqual(action.vectorActions[2], 3);
agent = agents[1] as TestAgent;
Assert.NotNull(agent);
action = agent.GetAction();
Assert.AreEqual(action.vectorActions[0], 4);
Assert.AreEqual(action.vectorActions[1], 5);
Assert.AreEqual(action.vectorActions[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 agentInfos = GetFakeAgentInfos();
var alloc = new TensorCachingAllocator();
var applier = new DiscreteActionOutputApplier(new[] { 2, 3 }, 0, alloc);
applier.Apply(inputTensor, agentInfos);
var agents = agentInfos;
var agent = agents[0] as TestAgent;
Assert.NotNull(agent);
var action = agent.GetAction();
Assert.AreEqual(action.vectorActions[0], 1);
Assert.AreEqual(action.vectorActions[1], 1);
agent = agents[1] as TestAgent;
Assert.NotNull(agent);
action = agent.GetAction();
Assert.AreEqual(action.vectorActions[0], 1);
Assert.AreEqual(action.vectorActions[1], 2);
alloc.Dispose();
}
[Test]
public void ApplyValueEstimate()
{
var inputTensor = new TensorProxy()
{
shape = new long[] { 2, 1 },
data = new Tensor(2, 1, new[] { 0.5f, 8f })
};
var agentInfos = GetFakeAgentInfos();
var applier = new ValueEstimateApplier();
applier.Apply(inputTensor, agentInfos);
var agents = agentInfos;
var agent = agents[0] as TestAgent;
Assert.NotNull(agent);
var action = agent.GetAction();
Assert.AreEqual(action.value, 0.5f);
agent = agents[1] as TestAgent;
Assert.NotNull(agent);
action = agent.GetAction();
Assert.AreEqual(action.value, 8);
}
}
}