不依赖状态信息的强化学习算法，实现与状态空间无关的稳定性能。
State-free Reinforcement Learning
- 设计无需状态空间信息的算法，仅依赖可达状态集
- 遗憾上界完全脱离状态空间大小，仅与可达状态相关
- 为无超参数调优的强化学习提供新思路，适合算法研究者
本文研究状态自由强化学习问题，即算法在与环境交互前无法获取状态信息。记可达状态集为 ${S}^Π := \{ s|\max_{π\in Π}q^{P, π}(s)>0 \}$，我们设计了一种算法，无需状态空间 $S$ 的任何信息，且其遗憾上界完全独立于 $S$，仅依赖于 ${S}^Π$。这被视为迈向参数自由强化学习的第一步，目标是设计无需超参数调优的强化学习算法。
原文摘要 · Abstract (English)
In this work, we study the \textit{state-free RL} problem, where the algorithm does not have the states information before interacting with the environment. Specifically, denote the reachable state set by ${S}^Π:= \{ s|\max_{π\in Π}q^{P, π}(s)>0 \}$, we design an algorithm which requires no information on the state space $S$ while having a regret that is completely independent of ${S}$ and only depend on ${S}^Π$. We view this as a concrete first step towards \textit{parameter-free RL}, with the goal of designing RL algorithms that require no hyper-parameter tuning.
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