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 NUnit.Framework;
using UnityEngine;
using UnityEditor;
using Unity.MLAgents.Inference;
using Unity.MLAgents.Sensors;
using Unity.Barracuda;
using System.Linq;
using Unity.MLAgents.Policies;
namespace Unity.MLAgents.Tests
{
[TestFixture]
public class ModelRunnerTest
{
const string k_continuous2vis8vec2actionPath = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/continuous2vis8vec2action.nn";
const string k_discrete1vis0vec_2_3action_recurrModelPath = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/discrete1vis0vec_2_3action_recurr.nn";
NNModel continuous2vis8vec2actionModel;
NNModel discrete1vis0vec_2_3action_recurrModel;
Test3DSensorComponent sensor_21_20_3;
Test3DSensorComponent sensor_20_22_3;
BrainParameters GetContinuous2vis8vec2actionBrainParameters()
{
var validBrainParameters = new BrainParameters();
validBrainParameters.VectorObservationSize = 8;
validBrainParameters.VectorActionSize = new [] { 2 };
validBrainParameters.NumStackedVectorObservations = 1;
validBrainParameters.VectorActionSpaceType = SpaceType.Continuous;
return validBrainParameters;
}
BrainParameters GetDiscrete1vis0vec_2_3action_recurrModelBrainParameters()
{
var validBrainParameters = new BrainParameters();
validBrainParameters.VectorObservationSize = 0;
validBrainParameters.VectorActionSize = new [] { 2, 3 };
validBrainParameters.NumStackedVectorObservations = 1;
validBrainParameters.VectorActionSpaceType = SpaceType.Discrete;
return validBrainParameters;
}
[SetUp]
public void SetUp()
{
continuous2vis8vec2actionModel = (NNModel)AssetDatabase.LoadAssetAtPath(k_continuous2vis8vec2actionPath, typeof(NNModel));
discrete1vis0vec_2_3action_recurrModel = (NNModel)AssetDatabase.LoadAssetAtPath(k_discrete1vis0vec_2_3action_recurrModelPath, typeof(NNModel));
var go = new GameObject("SensorA");
sensor_21_20_3 = go.AddComponent<Test3DSensorComponent>();
sensor_21_20_3.Sensor = new Test3DSensor("SensorA", 21, 20, 3);
sensor_20_22_3 = go.AddComponent<Test3DSensorComponent>();
sensor_20_22_3.Sensor = new Test3DSensor("SensorB", 20, 22, 3);
}
[Test]
public void TestModelExist()
{
Assert.IsNotNull(continuous2vis8vec2actionModel);
Assert.IsNotNull(discrete1vis0vec_2_3action_recurrModel);
}
[Test]
public void TestCreation()
{
var modelRunner = new ModelRunner(continuous2vis8vec2actionModel, GetContinuous2vis8vec2actionBrainParameters());
modelRunner.Dispose();
modelRunner = new ModelRunner(discrete1vis0vec_2_3action_recurrModel, GetDiscrete1vis0vec_2_3action_recurrModelBrainParameters());
modelRunner.Dispose();
}
[Test]
public void TestHasModel()
{
var modelRunner = new ModelRunner(continuous2vis8vec2actionModel, GetContinuous2vis8vec2actionBrainParameters(), InferenceDevice.CPU);
Assert.True(modelRunner.HasModel(continuous2vis8vec2actionModel, InferenceDevice.CPU));
Assert.False(modelRunner.HasModel(continuous2vis8vec2actionModel, InferenceDevice.GPU));
Assert.False(modelRunner.HasModel(discrete1vis0vec_2_3action_recurrModel, InferenceDevice.CPU));
modelRunner.Dispose();
}
[Test]
public void TestRunModel()
{
var brainParameters = GetDiscrete1vis0vec_2_3action_recurrModelBrainParameters();
var modelRunner = new ModelRunner(discrete1vis0vec_2_3action_recurrModel, brainParameters);
var info1 = new AgentInfo();
info1.episodeId = 1;
modelRunner.PutObservations(info1, new [] { sensor_21_20_3.CreateSensor() }.ToList());
var info2 = new AgentInfo();
info2.episodeId = 2;
modelRunner.PutObservations(info2, new [] { sensor_21_20_3.CreateSensor() }.ToList());
modelRunner.DecideBatch();
Assert.IsNotNull(modelRunner.GetAction(1));
Assert.IsNotNull(modelRunner.GetAction(2));
Assert.IsNull(modelRunner.GetAction(3));
Assert.AreEqual(brainParameters.VectorActionSize.Count(), modelRunner.GetAction(1).Count());
modelRunner.Dispose();
}
}
}