Provably Efficient Exploration in Constrained Reinforcement Learning:Posterior Sampling Is All You Need

Danil Provodin, Pratik Gajane, Mykola Pechenizkiy +1

We present a new algorithm based on posterior sampling for learning in constrained Markov decision processes (CMDP) in the infinite-horizon undiscounted setting. The algorithm achi…