Markov Decision Processes

Optimistic PAC Reinforcement Learning: the Instance-Dependent View
Optimistic algorithms have been extensively studied for regret minimization in episodic tabular Markov Decision Processes (MDPs), both …
Andrea Tirinzoni, Aymen Al Marjani, Emilie Kaufmann
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Near Instance-Optimal PAC Reinforcement Learning for Deterministic MDPs
In probably approximately correct (PAC) reinforcement learning (RL), an agent is required to identify an $\epsilon$-optimal policy with …
Andrea Tirinzoni, Aymen Al Marjani, Emilie Kaufmann
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Navigating to the Best Policy in Markov Decision Processes
We investigate the classical active pure exploration problem in Markov Decision Processes, where the agent sequentially selects actions …
Aymen Al Marjani, Aurélien Garivier, Alexandre Proutiere
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Adaptive Sampling for Best Policy Identification in Markov Decision Processes
We investigate the problem of best-policy identification in discounted Markov Decision Processes (MDPs) when the learner has access to …
Aymen Al Marjani, Alexandre Proutiere
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