Is Reinforcement Learning More Difficult Than Bandits? A Near-optimal Algorithm Escaping the Curse of Horizon

• URL: http://arxiv.org/abs/2009.13503v2
• Date: Wed, 30 Jun 2021 07:06:10 GMT
• Title: Is Reinforcement Learning More Difficult Than Bandits? A Near-optimal Algorithm Escaping the Curse of Horizon
• Authors: Zihan Zhang, Xiangyang Ji, Simon S. Du
• Abstract summary: Episodic reinforcement learning generalizes contextual bandits. Long planning horizon and unknown state-dependent transitions pose little additional difficulty on sample complexity. We show MVP enjoys an $left(left(sqrtSAK + S2Aright)$ regret, approaching the $Omegaleft(sqrtSAK + S2Aright)$ lower bound of emphcontextual bandits up to logarithmic terms.
• Score: 88.75843804630772
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Episodic reinforcement learning and contextual bandits are two widely studied sequential decision-making problems. Episodic reinforcement learning generalizes contextual bandits and is often perceived to be more difficult due to long planning horizon and unknown state-dependent transitions. The current paper shows that the long planning horizon and the unknown state-dependent transitions (at most) pose little additional difficulty on sample complexity. We consider the episodic reinforcement learning with $S$ states, $A$ actions, planning horizon $H$, total reward bounded by $1$, and the agent plays for $K$ episodes. We propose a new algorithm, \textbf{M}onotonic \textbf{V}alue \textbf{P}ropagation (MVP), which relies on a new Bernstein-type bonus. Compared to existing bonus constructions, the new bonus is tighter since it is based on a well-designed monotonic value function. In particular, the \emph{constants} in the bonus should be subtly setting to ensure optimism and monotonicity. We show MVP enjoys an $O\left(\left(\sqrt{SAK} + S^2A\right) \poly\log \left(SAHK\right)\right)$ regret, approaching the $\Omega\left(\sqrt{SAK}\right)$ lower bound of \emph{contextual bandits} up to logarithmic terms. Notably, this result 1) \emph{exponentially} improves the state-of-the-art polynomial-time algorithms by Dann et al. [2019] and Zanette et al. [2019] in terms of the dependency on $H$, and 2) \emph{exponentially} improves the running time in [Wang et al. 2020] and significantly improves the dependency on $S$, $A$ and $K$ in sample complexity.

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