Poster
Q-learning with Logarithmic Regret
Kunhe Yang · Lin Yang · Simon Du
Keywords: [ Deep Learning ] [ Reinforcement Learning ]
Abstract:
This paper presents the first non-asymptotic result showing a model-free algorithm can achieve logarithmic cumulative regret for episodic tabular reinforcement learning if there exists a strictly positive sub-optimality gap. We prove that the optimistic Q-learning studied in [Jin et al. 2018] enjoys a O(SA⋅poly(H)Δminlog(SAT)) cumulative regret bound where S is the number of states, A is the number of actions, H is the planning horizon, T is the total number of steps, and Δmin is the minimum sub-optimality gap of the optimal Q-function. This bound matches the information theoretical lower bound in terms of S,A,T up to a log(SA) factor. We further extend our analysis to the discounted setting and obtain a similar logarithmic cumulative regret bound.
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