| CORL: Research-oriented deep offline reinforcement learning library D Tarasov, A Nikulin, D Akimov, V Kurenkov, S Kolesnikov Advances in Neural Information Processing Systems 36, 30997-31020, 2023 | 91 | 2023 |
| Revisiting the minimalist approach to offline reinforcement learning D Tarasov, V Kurenkov, A Nikulin, S Kolesnikov Advances in Neural Information Processing Systems 36, 11592-11620, 2023 | 39 | 2023 |
| Anti-exploration by random network distillation A Nikulin, V Kurenkov, D Tarasov, S Kolesnikov International Conference on Machine Learning, 26228-26244, 2023 | 32 | 2023 |
| Minerl diamond 2021 competition: Overview, results, and lessons learned A Kanervisto, S Milani, K Ramanauskas, N Topin, Z Lin, J Li, J Shi, D Ye, ... NeurIPS 2021 Competitions and Demonstrations Track, 13-28, 2022 | 29 | 2022 |
| Q-ensemble for offline rl: Don't scale the ensemble, scale the batch size A Nikulin, V Kurenkov, D Tarasov, D Akimov, S Kolesnikov arXiv preprint arXiv:2211.11092, 2022 | 19 | 2022 |
| XLand-minigrid: Scalable meta-reinforcement learning environments in JAX A Nikulin, V Kurenkov, I Zisman, AS Agarkov, V Sinii, S Kolesnikov The Thirty-eight Conference on Neural Information Processing Systems …, 2024 | 18 | 2024 |
| In-context reinforcement learning for variable action spaces V Sinii, A Nikulin, V Kurenkov, I Zisman, S Kolesnikov arXiv preprint arXiv:2312.13327, 2023 | 13 | 2023 |
| Let offline rl flow: Training conservative agents in the latent space of normalizing flows D Akimov, V Kurenkov, A Nikulin, D Tarasov, S Kolesnikov arXiv preprint arXiv:2211.11096, 2022 | 13 | 2022 |
| Open rl benchmark: Comprehensive tracked experiments for reinforcement learning S Huang, Q Gallouédec, F Felten, A Raffin, RFJ Dossa, Y Zhao, ... arXiv preprint arXiv:2402.03046, 2024 | 9 | 2024 |
| Emergence of in-context reinforcement learning from noise distillation I Zisman, V Kurenkov, A Nikulin, V Sinii, S Kolesnikov arXiv preprint arXiv:2312.12275, 2023 | 8 | 2023 |
| Machine learning models for photonic crystals band diagram prediction and gap optimisation A Nikulin, I Zisman, M Eich, AY Petrov, A Itin Photonics and Nanostructures-Fundamentals and Applications 52, 101076, 2022 | 8 | 2022 |
| Katakomba: Tools and benchmarks for data-driven nethack V Kurenkov, A Nikulin, D Tarasov, S Kolesnikov Advances in Neural Information Processing Systems 36, 52969-52990, 2023 | 3 | 2023 |
| N-Gram Induction Heads for In-Context RL: Improving Stability and Reducing Data Needs I Zisman, A Nikulin, V Sinii, D Tarasov, N Lyubaykin, A Polubarov, ... arXiv preprint arXiv:2411.01958, 2024 | 1 | 2024 |
| XLand-100B: A Large-Scale Multi-Task Dataset for In-Context Reinforcement Learning A Nikulin, I Zisman, A Zemtsov, V Kurenkov arXiv preprint arXiv:2406.08973, 2024 | 1 | 2024 |
| Object-Centric Latent Action Learning A Klepach, A Nikulin, I Zisman, D Tarasov, A Derevyagin, A Polubarov, ... arXiv preprint arXiv:2502.09680, 2025 | | 2025 |
| Latent Action Learning Requires Supervision in the Presence of Distractors A Nikulin, I Zisman, D Tarasov, N Lyubaykin, A Polubarov, I Kiselev, ... arXiv preprint arXiv:2502.00379, 2025 | | 2025 |
| Vintix: Action Model via In-Context Reinforcement Learning A Polubarov, N Lyubaykin, A Derevyagin, I Zisman, D Tarasov, A Nikulin, ... arXiv preprint arXiv:2501.19400, 2025 | | 2025 |
Vladislav Kurenkovdunnolab, AIRIПідтверджена електронна адреса в innopolis.ru
