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To overcome the curses of dimensionality and modeling of Dynamic Programming(DP) methods to solve Markov Decision Process (MDP) problems, ReinforcementLearning (RL) methods are adopted in practice. Contrary to traditional RLalgorithms which do not consider the structural properties of the optimalpolicy, we propose a structure-aware learning algorithm to exploit the orderedmulti-threshold structure of the optimal policy, if any. We prove theasymptotic convergence of the proposed algorithm to the optimal policy. Due tothe reduction in the policy space, the proposed algorithm provides remarkableimprovements in storage and computational complexities over classical RLalgorithms. Simulation results establish that the proposed algorithm convergesfaster than other RL algorithms.

Online Reinforcement Learning of Optimal Threshold Policies for Markov Decision Processes