关于实用的强化学习:相邻的不确定性集和双代理算法
IEEE transactions on neural networks and learning systems
|April 15, 2024
概括
强有力的强化学习 (RRL) 现在扩展到连续空间. 新的算法通过优化最坏情况下的性能来确保强大的政策,克服表式方法的局限性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 控制理论 控制理论
背景情况:
- 强有力的强化学习 (RRL) 在马尔科夫决策过程 (MDP) 的不确定性集中优化了针对最坏情景的政策.
- 目前的RRL方法仅限于表格设置,阻碍了在复杂的连续状态/动作空间中的应用.
- 现实世界的部署面临挑战,因为培训模拟与实际环境之间的潜在不匹配.
研究的目的:
- 将强化的强化学习算法扩展到连续状态和动作空间.
- 开发一种新的RRL方法,保证与现有方法相比提高了稳定性.
- 解决当前RRL技术在处理复杂环境中的局限性.
主要方法:
- 构建了一个详细的不确定性集,可信的扰乱的MDPs.
- 建议用于具有有限时间误差限制的表格设置的相邻强大的Q学习 (ARQ学习).
- 引入了一种带有悲观因素的双代理方法,以使扩展到连续空间.
主要成果:
- ARQ-Learning显示了与Q-learning和Robust-Q相似的趋同,具有增强的稳定性保证.
- 这种新的双代理方法首次成功地将无模型的RRL扩展到连续状态/动作空间.
- 实验验证证了在连续环境中提出的算法的有效性.
结论:
- 开发的RRL算法有效地处理连续状态/动作空间,这与之前的工作相比是显著的进步.
- 双代理方法为优化复杂,不确定的环境中的政策提供了强大的解决方案.
- 这项研究为在现实世界应用中应用强化学习 (robust reinforcement learning) 开辟了新的途径.
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