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We propose an algorithm for tabular episodic reinforcement learning withconstraints. We provide a modular analysis with strong theoretical guaranteesfor settings with concave rewards and convex constraints, and for settings withhard constraints (knapsacks). Most of the previous work in constrainedreinforcement learning is limited to linear constraints, and the remaining workfocuses on either the feasibility question or settings with a single episode.Our experiments demonstrate that the proposed algorithm significantlyoutperforms these approaches in existing constrained episodic environments.

Constrained episodic reinforcement learning in concave-convex and knapsack settings