Logarithmic Regret Bounds for Continuous-Time Average-Reward Markov Decision Processes-Reference-Cited by-同舟云学术

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Logarithmic Regret Bounds for Continuous-Time Average-Reward Markov Decision Processes

Published:2024-09-10 Issue:5 Volume:62 Page:2529-2556
ISSN:0363-0129
Container-title:SIAM Journal on Control and Optimization
language:en
Short-container-title:SIAM J. Control Optim.

Author:

Gao Xuefeng¹,Zhou Xun Yu²

Affiliation:

1. Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Hong Kong, China.

2. Department of Industrial Engineering and Operations Research and The Data Science Institute, Columbia University, New York, NY 10027, USA.

Funder

Hong Kong Research Grant Council

Nie Center for Intelligent Asset Management

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Reference27 articles.

1. UCB revisited: Improved regret bounds for the stochastic multi-armed bandit problem

2. S. J. Bradtke and M. O. Duff, Reinforcement learning methods for continuous-time Markov decision, in Advances in Neural Information Processing Systems 7, Curran Associates, Red Hook, NY, 1995, 393.

3. Bandits With Heavy Tail

4. Optimal Adaptive Policies for Markov Decision Processes

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