首先返回,然后探索
Adrien Ecoffet1,2, Joost Huizinga3,4, Joel Lehman5,6
1Uber AI Labs, San Francisco, CA, USA. adrienecoffet@gmail.com.
Nature
|February 25, 2021
概括
Go-Explore算法通过记住状态并返回探索之前来改善强化学习. 这种方法解决了以前未解决的游戏,并提升了人工智能探索能力.
科学领域:
- 人工智能
- 机器学习
- 机器人技术
背景情况:
- 强化学习 (RL) 旨在实现自主决策,但与稀疏或欺骗性的奖励信号作斗争.
- 有效的环境探索对RL至关重要,但仍然是一个重大挑战.
- 现有的RL算法往往会忘记先前访问的状态,或者在探索新状态之前无法重新访问它们.
研究的目的:
- 在强化学习探索中应对脱离和脱轨的挑战.
- 为更有效的环境探索而设计的新算法Go-Explore.
- 在复杂的游戏和机器人任务中展示Go-Explore的有效性.
主要方法:
- 介绍了Go-Explore,这是一个算法家族,强调记住有希望的状态,并在探索之前返回它们.
- 将Go-Explore应用到以前未解决的Atari游戏和硬盘探索基准.
- 测试了 Go-Explore 在稀疏奖励的选择和放置机器人任务中.
- 综合目标化政策以进一步提高勘探效率和处理随机性.
主要成果:
- Go-Explore成功地解决了所有以前未解决的Atari游戏.
- 在硬探索游戏中实现了最先进的性能,对蒙特祖马的复仇和陷有了显著的改进.
- 在具有挑战性的稀有奖励机器人任务中展示了实际应用.
- 目标条件的政策提高了Go-Explore的勘探效率和稳定性.
结论:
- 记忆,返回和从状态探索的原则是RL探索的强大和通用方法.
- Go-Explore提供了显著的性能提升,
- 这些发现突显了结构化勘探策略在克服RL局限性的重要性.
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