Scalable Online Exploration via Coverability

• URL: http://arxiv.org/abs/2403.06571v2
• Date: Tue, 4 Jun 2024 20:12:47 GMT
• Title: Scalable Online Exploration via Coverability
• Authors: Philip Amortila, Dylan J. Foster, Akshay Krishnamurthy,
• Abstract summary: Exploration is a major challenge in reinforcement learning, especially for high-dimensional domains that require function approximation. We introduce a new objective, $L_Coverage, which generalizes previous exploration schemes and supports three fundamental desideratas. $L_Coverage enables the first computationally efficient model-based and model-free algorithms for online (reward-free or reward-driven) reinforcement learning in MDPs with low coverability.
• Score: 45.66375686120087
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Exploration is a major challenge in reinforcement learning, especially for high-dimensional domains that require function approximation. We propose exploration objectives -- policy optimization objectives that enable downstream maximization of any reward function -- as a conceptual framework to systematize the study of exploration. Within this framework, we introduce a new objective, $L_1$-Coverage, which generalizes previous exploration schemes and supports three fundamental desiderata: 1. Intrinsic complexity control. $L_1$-Coverage is associated with a structural parameter, $L_1$-Coverability, which reflects the intrinsic statistical difficulty of the underlying MDP, subsuming Block and Low-Rank MDPs. 2. Efficient planning. For a known MDP, optimizing $L_1$-Coverage efficiently reduces to standard policy optimization, allowing flexible integration with off-the-shelf methods such as policy gradient and Q-learning approaches. 3. Efficient exploration. $L_1$-Coverage enables the first computationally efficient model-based and model-free algorithms for online (reward-free or reward-driven) reinforcement learning in MDPs with low coverability. Empirically, we find that $L_1$-Coverage effectively drives off-the-shelf policy optimization algorithms to explore the state space.

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