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;
using System.Collections.Generic;
using System.Linq;
using NUnit.Framework;
using Unity.MLAgents.Actuators;
using Assert = UnityEngine.Assertions.Assert;
namespace Unity.MLAgents.Tests.Actuators
{
[TestFixture]
public class VectorActuatorTests
{
class TestActionReceiver : IActionReceiver, IHeuristicProvider
{
public ActionBuffers LastActionBuffers;
public int Branch;
public IList<int> Mask;
public ActionSpec ActionSpec { get; }
public bool HeuristicCalled;
public void OnActionReceived(ActionBuffers actionBuffers)
{
LastActionBuffers = actionBuffers;
}
public void WriteDiscreteActionMask(IDiscreteActionMask actionMask)
{
foreach (var actionIndex in Mask)
{
actionMask.SetActionEnabled(Branch, actionIndex, false);
}
}
public void Heuristic(in ActionBuffers actionBuffersOut)
{
HeuristicCalled = true;
}
}
[Test]
public void TestConstruct()
{
var ar = new TestActionReceiver();
var va = new VectorActuator(ar, ActionSpec.MakeDiscrete(1, 2, 3), "name");
Assert.IsTrue(va.ActionSpec.NumDiscreteActions == 3);
Assert.IsTrue(va.ActionSpec.SumOfDiscreteBranchSizes == 6);
Assert.IsTrue(va.ActionSpec.NumContinuousActions == 0);
var va1 = new VectorActuator(ar, ActionSpec.MakeContinuous(4), "name");
Assert.IsTrue(va1.ActionSpec.NumContinuousActions == 4);
Assert.IsTrue(va1.ActionSpec.SumOfDiscreteBranchSizes == 0);
Assert.AreEqual(va1.Name, "name-Continuous");
}
[Test]
public void TestOnActionReceived()
{
var ar = new TestActionReceiver();
var va = new VectorActuator(ar, ActionSpec.MakeDiscrete(1, 2, 3), "name");
var discreteActions = new[] { 0, 1, 1 };
var ab = new ActionBuffers(ActionSegment<float>.Empty,
new ActionSegment<int>(discreteActions, 0, 3));
va.OnActionReceived(ab);
Assert.AreEqual(ar.LastActionBuffers, ab);
va.ResetData();
Assert.AreEqual(va.ActionBuffers.ContinuousActions, ActionSegment<float>.Empty);
Assert.AreEqual(va.ActionBuffers.DiscreteActions, ActionSegment<int>.Empty);
}
[Test]
public void TestResetData()
{
var ar = new TestActionReceiver();
var va = new VectorActuator(ar, ActionSpec.MakeDiscrete(1, 2, 3), "name");
var discreteActions = new[] { 0, 1, 1 };
var ab = new ActionBuffers(ActionSegment<float>.Empty,
new ActionSegment<int>(discreteActions, 0, 3));
va.OnActionReceived(ab);
}
[Test]
public void TestWriteDiscreteActionMask()
{
var ar = new TestActionReceiver();
var va = new VectorActuator(ar, ActionSpec.MakeDiscrete(1, 2, 3), "name");
var bdam = new ActuatorDiscreteActionMask(new[] { va }, 6, 3);
var groundTruthMask = new[] { false, true, false, false, true, true };
ar.Branch = 1;
ar.Mask = new[] { 0 };
va.WriteDiscreteActionMask(bdam);
ar.Branch = 2;
ar.Mask = new[] { 1, 2 };
va.WriteDiscreteActionMask(bdam);
Assert.IsTrue(groundTruthMask.SequenceEqual(bdam.GetMask()));
}
[Test]
public void TestHeuristic()
{
var ar = new TestActionReceiver();
var va = new VectorActuator(ar, ActionSpec.MakeDiscrete(1, 2, 3), "name");
va.Heuristic(new ActionBuffers(Array.Empty<float>(), va.ActionSpec.BranchSizes));
Assert.IsTrue(ar.HeuristicCalled);
}
}
}