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A recent goal in the Reinforcement Learning (RL) framework is to choose asequence of actions or a policy to maximize the reward collected or minimizethe regret incurred in a finite time horizon. For several RL problems inoperation research and optimal control, the optimal policy of the underlyingMarkov Decision Process (MDP) is characterized by a known structure. Thecurrent state of the art algorithms do not utilize this known structure of theoptimal policy while minimizing regret. In this work, we develop new RLalgorithms that exploit the structure of the optimal policy to minimize regret.Numerical experiments on MDPs with structured optimal policies show that ouralgorithms have better performance, are easy to implement, have a smallerrun-time and require less number of random number generations.

Reinforcement Learning algorithms for regret minimization in structured Markov Decision Processes