UBG:一个虚幻的战场基准与对象意识层次的近接政策优化
概括
深度强化学习 (DRL) 面临着现实世界的挑战. 我们创建了一个3D FPS游戏Unreal BattleGround (UBG),并开发了对象意识层次近接策略优化 (OaH-PPO) 方法来提高DRL性能.
科学领域:
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
背景情况:
- 深度强化学习 (DRL) 在模拟中显示出希望,但由于环境限制,它在现实应用中扎.
- 现有的模拟环境缺乏高级DRL研究所需的视觉保真度,复杂性和任务多样性.
- 弥合模拟和现实世界DRL性能之间的差距需要更复杂和更现实的训练场.
研究的目的:
- 开发一种新的,现实的3D模拟环境,以推进深度强化学习.
- 引入一个新的DRL算法,旨在处理复杂的,开放世界的场景.
- 评估拟议的环境和算法的有效性,使DRL代理人能够在人类层面做出决策.
主要方法:
- 不真实的战场 (UBG) 的开发,一个使用不真实的引擎的3D开放世界第一人称射击游戏,具有可变的复杂性,随机场景和多样化的任务.
- 关于对象意识层次近接政策优化 (OaH-PPO) 方法的建议,该方法包含两个层次的选择控制和子任务掌握层次结构.
- 集成一个对象感知模块用于深度检测,基于潜力的内在奖励塑造用于探索,并回暖模仿学习用于引导初始化.
主要成果:
- 在复杂的场景中,UBG环境证明了用于训练和测试DRL代理人的广泛适用性.
- 在UBG基准中,OaH-PPO方法显著提高了DLR剂的性能.
- 实验结果验证了提出的层次方法及其支持模块的有效性.
结论:
- 虚幻战场 (UBG) 提供了一个强大而现实的平台,用于克服复杂环境中的DRL限制.
- 对象意识层次近接策略优化 (OaH-PPO) 方法为改善DRL代理能力提供了有效的解决方案.
- 开发的UBG环境和OaH-PPO算法代表了在实际应用中部署DRL的重要一步.
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