在深度强化学习中学习意图意识的政策
1College of Artificial Intelligence, Tianjin University of Science and Technology, Tianjin 300457, P.R.C. tingting@tust.edu.cn.
Neural computation
|July 31, 2023
概括
本研究介绍了深度强化学习 (DRL) 代理人的意图意识政策学习. 新方法允许代理商将类似人类的意图纳入他们的决策,以提高控制能力.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 机器人技术 机器人技术 机器人技术
背景情况:
- 深度强化学习 (DRL) 代理优化基于状态,内存和参数的策略.
- 人类决策过程中包含的意图 (如速度) 超出了传统的DRL因素.
研究的目的:
- 为DRL代理人开发一种意图意识的政策学习方法.
- 使代理人能够选择包含特定意图的行动,模仿人类行为.
主要方法:
- 通过将意向信息整合到政策模型中,正式制定了意向意识政策.
- 通过最大化累积奖励和意图和行动之间的相互信息 (MI) 来优化政策.
- 推导出MI目标的高效近似,用于实际实施.
主要成果:
- 在控制任务中证明了意图意识政策的有效性.
- 在经典的MuJoCo和多目标链路步行任务中展示了改进的代理性能.
结论:
- 拟议的意图意识政策学习方法提高了DRL代理人的决策能力.
- 结合意图使代理人的行为更像人,更适应.
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