深入研究无模型的强化学习,用于水下运动:理论和实践
Yusheng Jiao1, Feng Ling1, Sina Heydari2
1University of Southern California Viterbi School of Engineering, 920 Downey Way, Los Angeles, California, 90089-0111, United States.
Bioinspiration & biomimetics
|February 23, 2026
概括
深度强化学习 (RL) 可以为水生动物和水下机器人创建传感运动策略. 本教程介绍了RL对体内代理,重点关注生物灵感机器人设计和理解动物行为的演员批判方法.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算神经科学是一种神经科学.
- 人工智能的人工智能
背景情况:
- 水生动物和水下机器人需要复杂的感觉运动控制来完成任务,如在复杂的环境中导航和捕食.
- 深度强化学习 (RL) 提供了一个强大的框架,用于在模拟的物理世界中合成体现的代理人的控制政策.
研究的目的:
- 为在水下环境中体现的物质提供无模型强化学习的独立介绍.
- 突出物理建模选择在制定RL问题的作用.
- 为应用RL提供指导方针,以了解动物行为和设计生物灵感机器人.
主要方法:
- 专注于无模型强化学习中的关键演员方法.
- 介绍了RL的数学公式,强调物理建模.
- 讨论了演员-关键算法的实际实施方面.
主要成果:
- 提供RL控制游泳者的例子.
- 为选择与生物行为一致的观察,行动和奖励提供了指导方针.
- 展示了RL在探索关于生物和机器人系统反控制的假设中的实用性.
结论:
- 强化学习为研究自然和人工水生系统中的感觉运动控制提供了一个多功能框架.
- 本教程为研究人员提供了基础知识和实际考虑,以便将RL应用于体现的水下物质.
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