ОМНЖурнал вычислительной математики и математической физики Computational Mathematics and Mathematical Physics

  • ISSN (Print) 0044-4669
  • ISSN (Online) 3034-533

OPTIMAL APPROXIMATION OF AVERAGE REWARD MARKOV DECISION PROCESSES

Код статьи
S0044466925030074-1
DOI
10.31857/S0044466925030074
Тип публикации
Статья
Статус публикации
Опубликовано
Авторы
Том/ Выпуск
Том 65 / Номер выпуска 3
Страницы
325-337
Аннотация
Журнал вычислительной математики и математической физики, OPTIMAL APPROXIMATION OF AVERAGE REWARD MARKOV DECISION PROCESSES
Ключевые слова
Дата публикации
17.09.2025
Год выхода
2025
Всего подписок
0
Всего просмотров
26

Библиография

  1. 1. Gheshlaghi Azar M., Munos R., Kappen H.J. Minimax PAC bounds on the sample complexity of reinforcement learning with a generative model. Machine learning, vol. 91 (2013), pp. 325–349.
  2. 2. Zurek M., Chen Y. Span-Based Optimal Sample Complexity for Average Reward MDPs. arXiv preprint arXiv:2311.13469.
  3. 3. Tuynman A., Degenne R., Kaufmann E. Finding good policies in average-reward Markov Decision Processes without prior knowledge. arXiv preprint arXiv:2405.17108.
  4. 4. Wang S., Blanchet J., Glynn P. Optimal sample complexity for average reward markov decision processes. arXiv preprint arXiv:2310.08833.
  5. 5. Bartlett P.L., Tewari A. REGAL: A regularization based algorithm for reinforcement learning in weakly communicating MDPs. arXiv preprint arXiv:1205.2661.
  6. 6. Wang J., Wang M., Yang L.F. Near sample-optimal reduction-based policy learning for average reward mdp. arXiv preprint arXiv:2212.00603.
  7. 7. Goyal V., Grand-Clement J. A first-order approach to accelerated value iteration. Operations Research, vol. 71, no. 2 (2023), pp. 517–535.
  8. 8. Grand-Clement J. From convex optimization to MDPs: A review of first-order, second-order and quasi-Newton methods for MDPs. arXiv preprint arXiv:2104.10677.
  9. 9. Farahmand A.m., Ghavamzadeh M. PID accelerated value iteration algorithm. In International Conference on Machine Learning. PMLR, pp. 3143–3153.
  10. 10. Weissman T., Ordentlich E., Seroussi G., Verdu S., Weinberger M.J. Inequalities for the L1 deviation of the empirical distribution. Hewlett-Packard Labs, Tech. Rep (2003), p. 125.
  11. 11. Singh S.P., Yee R.C. An upper bound on the loss from approximate optimal-value functions. Machine Learning, vol. 16 (1994), pp. 227–233.
  12. 12. Li G., Wei Y., Chi Y., Gu Y., Chen Y. Breaking the sample size barrier in model-based reinforcement learning with a generative model. Advances in neural information processing systems, vol. 33 (2020), pp. 12 861–12 872.
  13. 13. Puterman M.L. Markov decision processes: discrete stochastic dynamic programming. John Wiley & Sons, 2014.
  14. 14. Wang S., Blanchet J., Glynn P. Optimal Sample Complexity of Reinforcement Learning for Mixing Discounted Markov Decision Processes. arXiv preprint arXiv:2302.07477.
  15. 15. Li T., Wu F., Lan G. Stochastic first-order methods for average-reward markov decision processes. arXiv preprint arXiv:2205.05800.
  16. 16. Jin Y., Sidford A. Towards tight bounds on the sample complexity of average-reward MDPs. In International Conference on Machine Learning. PMLR, pp. 5055–5064.
  17. 17. Tiapkin D., Gasnikov A. Primal-dual stochastic mirror descent for MDPs. In International Conference on Artificial Intelligence and Statistics. PMLR, pp. 9723–9740.
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