基因工作者:一个端到端的机器人强化学习方法,使用协作生成器和工人网络
Hao Wang1, Hengyu Man2, Wenxue Cui3
1Faculty of Computing, Harbin Institute of Technology, Harbin, 150001, Heilongjiang, China; College of Engineering, City University of Hong Kong, Kowloon, 999077, Hong Kong Special Administrative Region of China.
概括
这项研究引入了一种用于强化学习的新型神经技能表示,使机器人能够更有效地学习复杂的行为. 基因工作者算法在机器人任务中取得了90%以上的成功,大大改善了现有的方法.
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
- 机器学习 机器学习
背景情况:
- 当前的强化学习 (RL) 技能是动作序列的有限抽象.
- 在RL中固定的网络参数阻碍了与元学习和大型语言模型的集成.
- 对于先进的自主代理人来说,需要更灵活的技能代表.
研究的目的:
- 提出一种基于跨层神经元激活的新型神经技能表示.
- 为了引入一个端到端的机器人强化学习算法,GeneWorker,利用这种表示.
- 为了使可适应的网络参数能够在各种环境条件下改善决策.
主要方法:
- 开发了一种独特的神经技能表示,抽象神经元激活.
- 设计了一个GeneWorker算法与协作生成器和工人子网络.
- 发电机产生多空间神经技能;工人整合技能以适应网络参数.
主要成果:
- 在连续的机器人任务中,GeneWorker的平均成功率超过了90.67%.
- 与最先进的方法相比,该算法显示出更高的性能.
- 在选择和放置任务中实现了至少54%的改进.
结论:
- 拟议的神经技能表示增强了自主代理人的RL能力.
- 基因工作者为机器人强化学习提供了一种灵活有效的方法.
- 这种方法促进了可适应的网络参数,以在动态环境中提供稳健的性能.
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