Document Type

Conference Proceeding

Publication Title

Proceedings of Machine Learning Research

Abstract

We address the challenge of exploration in reinforcement learning (RL) when the agent operates in an unknown environment with sparse or no rewards. In this work, we study the maximum entropy exploration problem of two different types. The first type is visitation entropy maximization previously considered by Hazan et al. (2019) in the discounted setting. For this type of exploration, we propose a game-theoretic algorithm that has Oe(H3S2A/ε2) sample complexity thus improving the ε-dependence upon existing results, where S is a number of states, A is a number of actions, H is an episode length, and ε is a desired accuracy. The second type of entropy we study is the trajectory entropy. This objective function is closely related to the entropy-regularized MDPs, and we propose a simple algorithm that has a sample complexity of order Oe(poly(S, A, H)/ε). Interestingly, it is the first theoretical result in RL literature that establishes the potential statistical advantage of regularized MDPs for exploration. Finally, we apply developed regularization techniques to reduce sample complexity of visitation entropy maximization to Oe(H2SA/ε2), yielding a statistical separation between maximum entropy exploration and reward-free exploration.

First Page

34161

Last Page

34221

Publication Date

7-2023

Keywords

Entropy maximization, Faster rates, Game-theoretic, Maximum-entropy, Number of state, Objective functions, Reinforcement learnings, Sample complexity, SIMPLE algorithm, Unknown environments

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Open Access version from PMLR

Uploaded on June 21, 2024

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