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Provably Efficient Exploration in Constrained Reinforcement
  Learning:Posterior Sampling Is All You Need

Provably Efficient Exploration in Constrained Reinforcement Learning:Posterior Sampling Is All You Need

27 September 2023
Danil Provodin
Pratik Gajane
Mykola Pechenizkiy
M. Kaptein
ArXivPDFHTML

Papers citing "Provably Efficient Exploration in Constrained Reinforcement Learning:Posterior Sampling Is All You Need"

2 / 2 papers shown
Title
Optimistic Posterior Sampling for Reinforcement Learning with Few
  Samples and Tight Guarantees
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees
D. Tiapkin
Denis Belomestny
Daniele Calandriello
Eric Moulines
Rémi Munos
A. Naumov
Mark Rowland
Michal Valko
Pierre Menard
36
8
0
28 Sep 2022
Learning Infinite-Horizon Average-Reward Markov Decision Processes with
  Constraints
Learning Infinite-Horizon Average-Reward Markov Decision Processes with Constraints
Liyu Chen
R. Jain
Haipeng Luo
57
25
0
31 Jan 2022
1