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VCSAP:基于国家行动对访问次数的在线强化学习探索方法
Ruikai Zhou1, Wenbo Zhu1, Shuai Han2
1Key Laboratory of Symbolic Computation and Knowledge Engineering (Jilin University), Changchun 130012, China; College of Computer Science and Technology, Jilin University, Changchun 130012, China.
概括
一种新的探索方法,国家-行动对的访问数 (VCSAP),在稀疏的奖励环境中改进了在线强化学习. 它通过考虑新状态和新动作来增强代理探索,优于现有方法.
科学领域:
- 强化学习是一种强化学习.
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 使用内在奖励的在线强化学习 (RL) 策略对于稀疏或欺骗性的奖励环境是有效的.
- 基于计数的探索方法,如州访问计数,提供了内在的奖励,但可以过度探索州或州动作对,从而导致本地最佳.
- 现有的方法往往只专注于状态新性,忽视了行动新性.
研究的目的:
- 提出一种基于计数的新型探索方法,即国家-行动对 (VCSAP) 的访问计数,用于在线强化学习.
- 解决现有方法在处理稀疏奖励和防止局部最佳的限制.
- 通过考虑状态和动作新性来增强代理探索.
主要方法:
- 开发了VCSAP,这是一种基于计数的方法,可以跟踪单个州和州行动对的访问计数.
- 集成的VCSAP与已建立的RL算法:近接政策优化 (PPO) 和信托地区政策优化 (TRPO).
- 针对探索基线进行比较实验,包括随机网络蒸,对MuJoCo和稀疏的MuJoCo基准任务进行比较实验.
主要成果:
- 通过VCSAP增强的PPO (PPO-VCSAP) 和TRPO (TRPO-VCSAP) 在多个MuJoCo环境中表现得更好.
- 与随机网络蒸基线相比,PPO-VCSAP和TRPO-VCSAP的性能增长分别为18%和8%.
- 该方法有效地驱使代理商探索新状态并选择新动作,减轻了局部最佳问题的问题.
结论:
- VCSAP是一种有效的基于计数的探索策略,用于在线强化学习,特别是在具有挑战性的稀疏奖励设置中.
- 拟议的方法通过平衡状态和动作新性来增强探索,从而在像MuJoCo.Co.这样的复杂环境中获得更好的性能.
- 对于PPO和TRPO算法的现有勘探基线,VCSAP提供了显著的改进.
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