state-free reinforcement learning
State-free Reinforcement Learning
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 \mathcal{S} \Pi: \{ s \max_{\pi\in \Pi}q {P, \pi}(s) 0 \}, we design an algorithm which requires no information on the state space S while having a regret that is completely independent of \mathcal{S} and only depend on \mathcal{S} \Pi . 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.