Poster
State-free Reinforcement Learning
Mingyu Chen · Aldo Pacchiano · Xuezhou Zhang
West Ballroom A-D #6702
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 , we design an algorithm which requires no information on the state space while having a regret that is completely independent of and only depend on . 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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