Breaking the sample size barrier in model-based reinforcement learning with a generative model
| Year of publication: |
2024
|
|---|---|
| Authors: | Li, Gen ; Wei, Yuting ; Chi, Yuejie ; Chen, Yuxin |
| Published in: |
Operations research. - Linthicum, Md. : INFORMS, ISSN 1526-5463, ZDB-ID 2019440-7. - Vol. 72.2024, 1, p. 203-221
|
| Subject: | generative model | Machine Learning and Data Science | minimaxity | model-based reinforcement learning | policy evaluation | Künstliche Intelligenz | Artificial intelligence | Lernen | Learning | Lernprozess | Learning process | Theorie | Theory | Stichprobenerhebung | Sampling |
-
Is Q-learning minimax optimal? : a tight sample complexity analysis
Li, Gen, (2024)
-
Nonasymptotic analysis of Monte Carlo tree search
Shah, Devavrat, (2022)
-
Kallus, Nathan, (2022)
- More ...
-
Is Q-learning minimax optimal? : a tight sample complexity analysis
Li, Gen, (2024)
-
Model-based reinforcement learning for offline zero-sum Markov games
Yan, Yuling, (2024)
-
Fast global convergence of natural policy gradient methods with entropy regularization
Cen, Shicong, (2022)
- More ...