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Haque Ishfaq

ishfaqha@mila.quebec
Doctorat - McGill University
Superviseur⋅e principal⋅e
Doina Precup
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Publications

Offline Multitask Representation Learning for Reinforcement Learning
Haque Ishfaq
Thanh Nguyen-Tang
Songtao Feng
Raman Arora
Mengdi Wang
Ming Yin 0003
Doina Precup
2024-03-18
ArXiv (prépublication)
doi.org
arxiv.org
Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo
Haque Ishfaq
Qingfeng Lan
Pan Xu
A. Rupam Mahmood
Doina Precup
Animashree Anandkumar
Kamyar Azizzadenesheli
We present a scalable and effective exploration strategy based on Thompson sampling for reinforcement learning (RL). One of the key shortcom… (voir plus)ings of existing Thompson sampling algorithms is the need to perform a Gaussian approximation of the posterior distribution, which is not a good surrogate in most practical settings. We instead directly sample the Q function from its posterior distribution, by using Langevin Monte Carlo, an efficient type of Markov Chain Monte Carlo (MCMC) method. Our method only needs to perform noisy gradient descent updates to learn the exact posterior distribution of the Q function, which makes our approach easy to deploy in deep RL. We provide a rigorous theoretical analysis for the proposed method and demonstrate that, in the linear Markov decision process (linear MDP) setting, it has a regret bound of
2024-01-16
ICLR.cc/2024/Conference (poster)
doi.org
openreview.net