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Dimitrakakis, Christos
Nom
Dimitrakakis, Christos
Affiliation principale
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Professor
Email
christos.dimitrakakis@unine.ch
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Résultat de la recherche
Voici les éléments 1 - 2 sur 2
- PublicationAccès libreNear-optimal Bayesian Solution For Unknown Discrete Markov Decision Process(2019-06-20T06:32:36Z)
;Aristide Tossou; Debabrota BasuWe tackle the problem of acting in an unknown finite and discrete Markov Decision Process (MDP) for which the expected shortest path from any state to any other state is bounded by a finite number $D$. An MDP consists of $S$ states and $A$ possible actions per state. Upon choosing an action $a_t$ at state $s_t$, one receives a real value reward $r_t$, then one transits to a next state $s_{t+1}$. The reward $r_t$ is generated from a fixed reward distribution depending only on $(s_t, a_t)$ and similarly, the next state $s_{t+1}$ is generated from a fixed transition distribution depending only on $(s_t, a_t)$. The objective is to maximize the accumulated rewards after $T$ interactions. In this paper, we consider the case where the reward distributions, the transitions, $T$ and $D$ are all unknown. We derive the first polynomial time Bayesian algorithm, BUCRL{} that achieves up to logarithm factors, a regret (i.e the difference between the accumulated rewards of the optimal policy and our algorithm) of the optimal order $\tilde{\mathcal{O}}(\sqrt{DSAT})$. Importantly, our result holds with high probability for the worst-case (frequentist) regret and not the weaker notion of Bayesian regret. We perform experiments in a variety of environments that demonstrate the superiority of our algorithm over previous techniques. Our work also illustrates several results that will be of independent interest. In particular, we derive a sharper upper bound for the KL-divergence of Bernoulli random variables. We also derive sharper upper and lower bounds for Beta and Binomial quantiles. All the bound are very simple and only use elementary functions. - PublicationAccès libreNear-Optimal Online Egalitarian learning in General Sum Repeated Matrix Games(2019-06-04T17:43:08Z)
;Aristide Tossou; ;Jaroslaw RzepeckiKatja HofmannWe study two-player general sum repeated finite games where the rewards of each player are generated from an unknown distribution. Our aim is to find the egalitarian bargaining solution (EBS) for the repeated game, which can lead to much higher rewards than the maximin value of both players. Our most important contribution is the derivation of an algorithm that achieves simultaneously, for both players, a high-probability regret bound of order O(lnT−−−√3⋅T2/3) after any T rounds of play. We demonstrate that our upper bound is nearly optimal by proving a lower bound of Ω(T2/3) for any algorithm.