State-free Reinforcement Learning
<|reference_start|>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 ${S}^\Pi := \{ s|\max_{\pi\in \Pi}q^{P, \pi}(s)>0 \}$, we design an algorithm which requires no information on the state space $S$ while having a regret that is completely independent of ${S}$ and only depend on ${S}^\Pi$. 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.<|reference_end|>
@article{chen2024state-free,
    title={State-free Reinforcement Learning},
    author={Mingyu Chen, Aldo Pacchiano, Xuezhou Zhang},
    journal={arXiv preprint arXiv:2409.18439},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.18439},
    primaryClass={cs.LG cs.AI}
}