Online Convex Optimization in Adversarial Markov Decision Processes
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Proceedings of the 36th International Conference on Machine Learning, PMLR 97:54785486, 2019.
Abstract
We consider online learning in episodic loopfree Markov decision processes (MDPs), where the loss function can change arbitrarily between episodes, and the transition function is not known to the learner. We show $\tilde{O}(LX\sqrt{AT})$ regret bound, where $T$ is the number of episodes, $X$ is the state space, $A$ is the action space, and $L$ is the length of each episode. Our online algorithm is implemented using entropic regularization methodology, which allows to extend the original adversarial MDP model to handle convex performance criteria (different ways to aggregate the losses of a single episode) , as well as improve previous regret bounds.
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