持续强化学习用于四肢机器人运动机器人
Sibo Gai1,2, Shangke Lyu2, Hongyin Zhang2
1School of Computer Science, Fudan University, Shanghai 200433, China.
Entropy (Basel, Switzerland)
|January 26, 2024
概括
本研究引入了一种用于四足机器人持续强化学习 (RL) 的新方法. 该方法通过平衡可塑性和稳定性来增强机器人学习,这对于智能自主系统至关重要.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 持续学习对于机器人智能和自主性至关重要.
- 持续强化学习 (RL) 旨在使机器人能够顺序学习 (可塑性),同时保留过去的知识 (稳定性).
- 现有的RL方法在连续任务中与灾难性的遗忘和可塑性损失作斗争.
研究的目的:
- 为四足机器人开发一种持续增强学习方法,以平衡可塑性和稳定性.
- 尽量减少对原始RL学习过程的修改.
- 克服灾难性遗忘并保持以前学习的任务的性能.
主要方法:
- 使用Piggyback算法来识别和保护特定任务的参数.
- 重新启动未使用的参数以增强可塑性.
- 通过在软网络中最大化来鼓励政策网络探索.
主要成果:
- 拟议的方法使机器人能够进行顺序学习,而不会对先前的任务造成显著的性能下降.
- 与传统的持续学习算法相比,显示出更高的稳定性和较少的RL训练中断.
- 在机器人机动任务上的实验验证证证了该方法的有效性.
结论:
- 开发的持续RL方法成功地解决了机器人学习中的可塑性-稳定性困境.
- 该方法提供了一种稳定有效的方法,通过顺序任务获取来提高机器人的智能和自主性.
- 这项工作为现实世界机器人应用中的持续学习提供了强大的解决方案.
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