Variational Regret Bounds for Reinforcement Learning

14 May 2019Pratik GajaneRonald OrtnerPeter Auer

We consider undiscounted reinforcement learning in Markov decision processes (MDPs) where both the reward functions and the state-transition probabilities may vary (gradually or abruptly) over time. For this problem setting, we propose an algorithm and provide performance guarantees for the regret evaluated against the optimal non-stationary policy... (read more)

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