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 in the infinite-horizon undiscounted setting. The algorithm achieves near-optimal regret bounds while being advantageous empirically compared to the existing algorithms. Our main theoretical result is a Bayesian regret bound for each cost component of %24tilde{O} (DSsqrt{AT})%24 for any communicating CMDP with %24S%24 states%2C %24A%24 actions%2C and diameter %24D%24. This regret bound matches the lower bound in order of time horizon %24T%24 and is the best-known regret bound for communicating CMDPs achieved by a computationally tractable algorithm. Empirical results show that our posterior sampling algorithm outperforms the existing algorithms for constrained reinforcement learning.?quality=80&w=800)
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