Active Coverage for PAC Reinforcement Learning
This work addresses the challenge of data coverage in reinforcement learning, providing a flexible framework for online exploration tasks, though it appears incremental as it builds on existing PAC RL concepts.
The paper tackles the problem of active coverage in episodic Markov decision processes, where the goal is to interact with the environment to meet specific sampling requirements, and introduces an algorithm, CovGame, that nearly matches an instance-dependent lower bound on sample complexity, with applications in PAC reward-free exploration and best-policy identification.
Collecting and leveraging data with good coverage properties plays a crucial role in different aspects of reinforcement learning (RL), including reward-free exploration and offline learning. However, the notion of "good coverage" really depends on the application at hand, as data suitable for one context may not be so for another. In this paper, we formalize the problem of active coverage in episodic Markov decision processes (MDPs), where the goal is to interact with the environment so as to fulfill given sampling requirements. This framework is sufficiently flexible to specify any desired coverage property, making it applicable to any problem that involves online exploration. Our main contribution is an instance-dependent lower bound on the sample complexity of active coverage and a simple game-theoretic algorithm, CovGame, that nearly matches it. We then show that CovGame can be used as a building block to solve different PAC RL tasks. In particular, we obtain a simple algorithm for PAC reward-free exploration with an instance-dependent sample complexity that, in certain MDPs which are "easy to explore", is lower than the minimax one. By further coupling this exploration algorithm with a new technique to do implicit eliminations in policy space, we obtain a computationally-efficient algorithm for best-policy identification whose instance-dependent sample complexity scales with gaps between policy values.