Constrained Upper Confidence Reinforcement Learning
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Constrained Markov Decision Processes are a class of stochastic decision problems in which the decision maker must select a policy that satisfies auxiliary cost constraints. This paper extends upper confidence reinforcement learning for settings in which the reward function and the constraints, described by cost functions, are unknown a priori but the transition kernel is known. Such a setting is well-motivated by a number of applications including exploration of unknown, potentially unsafe, environments. We present an algorithm C-UCRL and show that it achieves sub-linear regret (O(T^3/4√(log(T/δ)))) with respect to the reward while satisfying the constraints even while learning with probability 1-δ. Illustrative examples are provided.
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