Poster
Online Convex Optimization in Adversarial Markov Decision Processes
Aviv Rosenberg · Yishay Mansour

Thu Jun 13th 06:30 -- 09:00 PM @ Pacific Ballroom #150

We consider online learning in episodic loop-free 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 $\tilde{O}(L|X|\sqrt{|A|T})$ 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.

Author Information

Aviv Rosenberg (Tel Aviv University)
Yishay Mansour (Google and Tel Aviv University)

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