Regret Bounds for Kernel-Based Reinforcement Learning

By Omar Darwiche Domingues, Pierre M'enard, Matteo Pirotta, Emilie Kaufmann, Michal Valko. 2020

22 citations recorded by the source corpus.

Subfields: Reinforcement Learning

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In Reinforcement Learning

1. Jonathan Lee
2. Aldo Pacchiano
3. Aadirupa Saha
4. Michal Valko
5. Pierre M'enard
6. Emilie Kaufmann
7. Omar Darwiche Domingues
8. Matteo Pirotta

Citations in the corpus

• Dueling RL: Reinforcement Learning with Trajectory Preferences (2021, background): “…2013; Dann and Brunskill, 2015; Dann et al., 2017; Domingues et al., 2020a; Lattimore and Hutter, 2012) and minimizing regret via balancing exploration and exploitation (Zhang and Ji, 2019; Azar et al., 2017; Ortner, 2020; Talebi and Maillard, 2018; Efroni et al., 2020; Domingues et al., 2020b).”
• A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces (2020, methodology): “As a sanity-check, we note that in stationary MDPs, for which ∆ = 0 , we recover the regret bound of Kernel-UCBVI 7 (Domingues et al., 2020) , which is optimal for d = 1 in the (stationary) bandit case (Bubeck et al., 2011).”