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Found 7 papers, 0 papers with code
Near-optimal Bayesian Solution For Unknown Discrete Markov Decision Process
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 $\tilde{\mathcal{O}}(\sqrt{DSAT})$.
Near-Optimal Online Egalitarian learning in General Sum Repeated Matrix Games
We study two-player general sum repeated finite games where the rewards of each player are generated from an unknown distribution.
Differential Privacy for Multi-armed Bandits: What Is It and What Is Its Cost?
We derive and contrast lower bounds on the regret of bandit algorithms satisfying these definitions.
Near-optimal Optimistic Reinforcement Learning using Empirical Bernstein Inequalities
We study model-based reinforcement learning in an unknown finite communicating Markov decision process.
Model-based Reinforcement Learning
reinforcement-learning
+1
Algorithms for Differentially Private Multi-Armed Bandits
This is a significant improvement over previous results, which only achieve poly-log regret $O(\epsilon^{-2} \log^{2} T)$, because of our use of a novel interval-based mechanism.
Probabilistic inverse reinforcement learning in unknown environments
To do so, we extend previous probabilistic approaches for inverse reinforcement learning in known MDPs to the case of unknown dynamics or opponents.
