State-free Reinforcement Learning

Mingyu Chen; Aldo Pacchiano; Xuezhou Zhang

NeurIPS 2024poster1 citations

State-free Reinforcement Learning

Mingyu Chen, Aldo Pacchiano, Xuezhou Zhang

Abstract

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.

Reinforcement LearningParameter-freeOnline Learning

BibTeX

@inproceedings{
chen2024statefree,
title={State-free Reinforcement Learning},
author={Mingyu Chen and Aldo Pacchiano and Xuezhou Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=SQicD307Oh}
}