Improved Analysis of UCRL2 with Empirical Bernstein Inequality

Exploration-exploitation tradeoff using variance estimates in multi-armed bandits

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Tuning bandit algorithms in stochastic environments

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Near-optimal regret bounds for reinforcement learning

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Minimax PAC bounds on the sample complexity of reinforcement learning with a generative model

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Near-optimal pac bounds for discounted mdps

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How hard is my mdp?” the distribution-norm to the rescue”

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Minimax regret bounds for reinforcement learning

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Efficient bias-span-constrained exploration-exploitation in reinforcement learning

Original

Fruit, R., Pirotta, M., Lazaric, A., and Ortner, R. (2018) · 2018

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Bandit algorithms

Lattimore, T. and Szepesvári, C. (2018) · 2018

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Variance-aware regret bounds for undiscounted reinforcement learning in mdps

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Exploration-exploitation dilemma in Reinforcement Learning under various form of prior knowledge

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