A Sliding-Window Algorithm for Markov Decision Processes with Arbitrarily Changing Rewards and Transitions

25 May 2018Pratik GajaneRonald OrtnerPeter Auer

We consider reinforcement learning in changing Markov Decision Processes where both the state-transition probabilities and the reward functions may vary over time. For this problem setting, we propose an algorithm using a sliding window approach and provide performance guarantees for the regret evaluated against the optimal non-stationary policy... (read more)

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