Constrained Upper Confidence Reinforcement Learning

26 Jan 2020Liyuan ZhengLillian J. Ratliff

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... (read more)

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