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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#if MLA_INPUT_TESTS && UNITY_2019_4_OR_NEWER
using NUnit.Framework;
using Unity.MLAgents.Actuators;
using Unity.MLAgents.Extensions.Input;
using UnityEngine;
using UnityEngine.InputSystem;
namespace Unity.MLAgents.Extensions.Tests.Runtime.Input
{
public class DoubleInputActionAdaptorTests : InputTestFixture
{
DoubleInputActionAdaptor m_Adaptor;
InputDevice m_Device;
InputControl<double> m_Control;
InputAction m_Action;
public override void Setup()
{
base.Setup();
const string kLayout = @"
{
""name"" : ""TestDevice"",
""extend"" : ""HID"",
""controls"" : [
{ ""name"" : ""button"", ""layout"" : ""Double"" }
]
}";
InputSystem.RegisterLayout(kLayout);
m_Device = InputSystem.AddDevice("TestDevice");
m_Control = (InputControl<double>)m_Device["button"];
m_Action = new InputAction("action", InputActionType.Value, "/TestDevice/button", null, null, "double");
m_Action.Enable();
m_Adaptor = new DoubleInputActionAdaptor();
}
public override void TearDown()
{
base.TearDown();
m_Adaptor = null;
}
[Test]
public void TestGenerateActionSpec()
{
var actionSpec = m_Adaptor.GetActionSpecForInputAction(new InputAction());
Assert.IsTrue(actionSpec.NumContinuousActions == 1);
}
[Test]
public void TestQueueEvent()
{
var actionBuffers = new ActionBuffers(new ActionSegment<float>(new[] { 1f }), ActionSegment<int>.Empty);
var context = new InputActuatorEventContext(1, m_Device);
using (context.GetEventForFrame(out var eventPtr))
{
m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
}
InputSystem.Update();
Assert.IsTrue(Mathf.Approximately(1f, (float)m_Action.ReadValue<double>()));
}
[Test]
public void TestWriteToHeuristic()
{
var actionBuffers = new ActionBuffers(new ActionSegment<float>(new[] { 1f }), ActionSegment<int>.Empty);
var context = new InputActuatorEventContext(1, m_Device);
using (context.GetEventForFrame(out var eventPtr))
{
m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
}
InputSystem.Update();
var buffer = new ActionBuffers(new ActionSegment<float>(new[] { 1f }), ActionSegment<int>.Empty);
m_Adaptor.WriteToHeuristic(m_Action, buffer);
Assert.IsTrue(Mathf.Approximately(buffer.ContinuousActions[0], 1f));
}
}
}
#endif // MLA_INPUT_TESTS && UNITY_2019_4_OR_NEWER