对于机器人操纵器跟踪控制的好奇心模型政策优化,在不确定的环境中以输入和进行输入和
Tu Wang1, Fujie Wang2, Zhongye Xie2
1College of Computer Science and Technology, Dongguan University of Technology, Dongguan, China.
Frontiers in neurorobotics
|July 11, 2024
概括
好奇心模型政策优化 (CMPO) 通过将好奇心与基于模型的强化学习相结合,在不确定的环境中增强机器人控制. 这种新的方法提高了跟踪性能和概括性,超过了传统和基线方法.
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
- 控制理论 控制理论
背景情况:
- 机器人控制在不确定的环境和输入和对现有方法具有挑战性.
- 基于模型的强化学习 (MBRL) 和传统控制器在最佳性能方面存在局限性.
- 需要先进的算法来提高跟踪准确性和概括性.
研究的目的:
- 提出一个新的算法框架,好奇心模型政策优化 (CMPO),用于增强机器人控制.
- 将好奇心驱动的探索与基于模型的方法相结合,以提高学习效率.
- 为了减少跟踪错误,提高机器人控制任务中的概括能力.
主要方法:
- 开发了一个结合好奇心和基于模型的方法 (CMPO) 的框架.
- 介绍了一种衡量积极和消极好奇心的指标.
- 使用受约束优化来更新好奇心比率以获得高效的代理培训.
- 定义了一个新性距离缓冲比,以减轻环境模型偏差.
- 在非线性奖励机器人环境中对传统控制器和基线MBRL算法进行模拟CMPO.
主要成果:
- 与传统和基线MBRL算法相比,CMPO表现出优异的跟踪性能.
- 拟议的算法在机器人控制任务中表现出增强的概括能力.
- 由好奇心驱动的方法和偏见减少技术提高了学习效率和表现.
结论:
- 在机器人控制方面,CMPO提供了显著的进步,特别是在不确定的和和的条件下.
- 好奇心和基于模型的学习的整合为复杂的控制问题提供了一个强大的框架.
- 开发的好奇心评估和偏差减少方法有效地提高了代理商的性能和概括性.
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