Provably Efficient Algorithms for Large-Scale Reinforcement Learning

Risultati finali

The Open Research Data Pilot will be prepared and submitted to the European Commission

Pubblicazioni

Efficient Global Planning in Large MDPs via Stochastic Primal-Dual Optimization

Autori: Gergely Neu, Nneka Okolo
Pubblicato in: Proceedings of The 34th International Conference on Algorithmic Learning Theory (ALT 2023), 2023
Editore: Proceedings of Machine Learning Research

Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits

Autori: Gergely Neu, Julia Olkhovskaya, Matteo Papini, Ludovic Schwartz
Pubblicato in: Advances in Neural Information Processing Systems 35 (NeurIPS 2022), 2022
Editore: NeurIPS foundation

Proximal Point Imitation Learning

Autori: Luca Viano, Angeliki Kamoutsi, Gergely Neu, Igor Krawczuk, Volkan Cevher
Pubblicato in: Advances in Neural Information Processing Systems 35 (NeurIPS 2022), 2022
Editore: NeurIPS foundation

Online learning with off-policy feedback

Autori: Germano Gabbianelli, Matteo Papini, Gergely Neu
Pubblicato in: Proceedings of The 34th International Conference on Algorithmic Learning Theory (ALT 2023), 2023
Editore: Proceedings of Machine Learning Research

Optimistic Planning by Regularized Dynamic Programming

Autori: Antoine Moulin, Gergely Neu
Pubblicato in: International Conference on Machine Learning (ICML 2022), 2023
Editore: Proceedings of Machine Learning Research

Generalization bounds via convex analysis

Autori: Gabor Lugosi, Gergely Neu
Pubblicato in: Proceedings of Thirty Fifth Conference on Learning Theory (COLT 2022), 2022
Editore: Proceedings of Machine Learning Research

Smoothing policies and safe policy gradients

Autori: Matteo Papini; Matteo Pirotta; Marcello Restelli
Pubblicato in: Machine Learning, Numero 111, 2022, Pagina/e 4081–4137, ISSN 1573-0565
Editore: Springer
DOI: 10.1007/s10994-022-06232-6

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