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;
using System.Collections.Generic;
using Unity.MLAgents;
using Unity.MLAgents.Policies;
using Unity.MLAgents.Sensors;
using Unity.MLAgents.Sensors.Reflection;
using NUnit.Framework;
using Unity.MLAgents.Actuators;
using UnityEngine;
using UnityEngine.TestTools;
namespace Tests
{
public class PublicApiAgent : Agent
{
public int numHeuristicCalls;
[Observable]
public float ObservableFloat;
public override void Heuristic(in ActionBuffers actionsOut)
{
numHeuristicCalls++;
base.Heuristic(actionsOut);
}
}
// Simple SensorComponent that sets up a StackingSensor
public class StackingComponent : SensorComponent
{
public SensorComponent wrappedComponent;
public int numStacks;
public override ISensor[] CreateSensors()
{
var wrappedSensors = wrappedComponent.CreateSensors();
var sensorsOut = new ISensor[wrappedSensors.Length];
for (var i = 0; i < wrappedSensors.Length; i++)
{
sensorsOut[i] = new StackingSensor(wrappedSensors[i], numStacks);
}
return sensorsOut;
}
}
public class RuntimeApiTest
{
[SetUp]
public static void Setup()
{
if (Academy.IsInitialized)
{
Academy.Instance.Dispose();
}
Academy.Instance.AutomaticSteppingEnabled = false;
}
[UnityTest]
public IEnumerator RuntimeApiTestWithEnumeratorPasses()
{
Academy.Instance.InferenceSeed = 1337;
var gameObject = new GameObject();
var behaviorParams = gameObject.AddComponent<BehaviorParameters>();
behaviorParams.BrainParameters.VectorObservationSize = 3;
behaviorParams.BrainParameters.NumStackedVectorObservations = 2;
behaviorParams.BrainParameters.VectorActionDescriptions = new[] { "Continuous1", "TestActionA", "TestActionB" };
behaviorParams.BrainParameters.ActionSpec = new ActionSpec(1, new[] { 2, 2 });
behaviorParams.BehaviorName = "TestBehavior";
behaviorParams.TeamId = 42;
behaviorParams.UseChildSensors = true;
behaviorParams.ObservableAttributeHandling = ObservableAttributeOptions.ExamineAll;
// Can't actually create an Agent with InferenceOnly and no model, so change back
behaviorParams.BehaviorType = BehaviorType.Default;
#if MLA_UNITY_PHSYICS_MODULE
var sensorComponent = gameObject.AddComponent<RayPerceptionSensorComponent3D>();
sensorComponent.SensorName = "ray3d";
sensorComponent.DetectableTags = new List<string> { "Player", "Respawn" };
sensorComponent.RaysPerDirection = 3;
// Make a StackingSensor that wraps the RayPerceptionSensorComponent3D
// This isn't necessarily practical, just to ensure that it can be done
var wrappingSensorComponent = gameObject.AddComponent<StackingComponent>();
wrappingSensorComponent.wrappedComponent = sensorComponent;
wrappingSensorComponent.numStacks = 3;
// ISensor isn't set up yet.
Assert.IsNull(sensorComponent.RaySensor);
#endif
// Make sure we can set the behavior type correctly after the agent is initialized
// (this creates a new policy).
behaviorParams.BehaviorType = BehaviorType.HeuristicOnly;
// Agent needs to be added after everything else is setup.
var agent = gameObject.AddComponent<PublicApiAgent>();
// DecisionRequester has to be added after Agent.
var decisionRequester = gameObject.AddComponent<DecisionRequester>();
decisionRequester.DecisionPeriod = 2;
decisionRequester.TakeActionsBetweenDecisions = true;
#if MLA_UNITY_PHSYICS_MODULE
// Initialization should set up the sensors
Assert.IsNotNull(sensorComponent.RaySensor);
#endif
// Let's change the inference device
var otherDevice = behaviorParams.InferenceDevice == InferenceDevice.CPU ? InferenceDevice.GPU : InferenceDevice.CPU;
agent.SetModel(behaviorParams.BehaviorName, behaviorParams.Model, otherDevice);
agent.AddReward(1.0f);
// skip a frame.
yield return null;
Academy.Instance.EnvironmentStep();
var actions = agent.GetStoredActionBuffers().DiscreteActions;
// default Heuristic implementation should return zero actions.
Assert.AreEqual(new ActionSegment<int>(new[] { 0, 0 }), actions);
Assert.AreEqual(1, agent.numHeuristicCalls);
Academy.Instance.EnvironmentStep();
Assert.AreEqual(1, agent.numHeuristicCalls);
Academy.Instance.EnvironmentStep();
Assert.AreEqual(2, agent.numHeuristicCalls);
}
}
}