Abstract
In this paper, we study model-based reinforcement learning in an unknown constrained Markov Decision Processes (CMDPs) with reset action. We propose an algorithm, Constrained-UCRL, which uses confidence interval like UCRL2, and solves linear programming problem to compute policy at the start of each episode. We show that Constrained-UCRL achieves sublinear regret bounds O(SA1/2T3/4) up to logarithmic factors with high probability for both the gain and the constraint violations.
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