| 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, 2024 | 77 | 2024 |
| Showing your offline reinforcement learning work: Online evaluation budget matters V Kurenkov, S Kolesnikov International Conference on Machine Learning, 11729-11752, 2022 | 25 | 2022 |
| Anti-exploration by random network distillation A Nikulin, V Kurenkov, D Tarasov, S Kolesnikov International Conference on Machine Learning, 26228-26244, 2023 | 24 | 2023 |
| Revisiting the minimalist approach to offline reinforcement learning D Tarasov, V Kurenkov, A Nikulin, S Kolesnikov Advances in Neural Information Processing Systems 36, 2024 | 23 | 2024 |
| Q-Ensemble for Offline RL: Don't Scale the Ensemble, Scale the Batch Size A Nikulin, V Kurenkov, D Tarasov, D Akimov, S Kolesnikov NeurIPS 2022, 3rd Offline RL Workshop: Offline RL as a ''Launchpad'', 2022 | 19 | 2022 |
| XLand-minigrid: Scalable meta-reinforcement learning environments in JAX A Nikulin, V Kurenkov, I Zisman, A Agarkov, V Sinii, S Kolesnikov arXiv preprint arXiv:2312.12044, 2023 | 16 | 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 NeurIPS 2022, 3rd Offline RL Workshop: Offline RL as a ''Launchpad'', 2022 | 12 | 2022 |
| In-context reinforcement learning for variable action spaces V Sinii, A Nikulin, V Kurenkov, I Zisman, S Kolesnikov arXiv preprint arXiv:2312.13327, 2023 | 8 | 2023 |
| 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 | 4 | 2023 |
| Prompts and Pre-Trained Language Models for Offline Reinforcement Learning D Tarasov, V Kurenkov, S Kolesnikov ICLR 2022, Workshop on Generalizable Policy Learning in Physical World, 2022 | 4 | 2022 |
| Katakomba: tools and benchmarks for data-driven NetHack V Kurenkov, A Nikulin, D Tarasov, S Kolesnikov Advances in Neural Information Processing Systems 36, 2024 | 2 | 2024 |
| Guiding Evolutionary Strategies by Differentiable Robot Simulators V Kurenkov, B Maksudov NeurIPS 2021, 4th Robot Learning Workshop, 2021 | 2 | 2021 |
| Learning stabilizing control policies for a tensegrity hopper with augmented random search V Kurenkov, H Hamed, S Savin 2020 International Conference on Industrial Engineering, Applications and …, 2020 | 2 | 2020 |
| Task-Oriented Language Grounding for Language Input with Multiple Sub-Goals of Non-Linear Order V Kurenkov, B Maksudov, A Khan arXiv preprint arXiv:1910.12354, 2019 | 2 | 2019 |
| XLand-100B: A Large-Scale Multi-Task Dataset for In-Context Reinforcement Learning A Nikulin, I Zisman, A Zemtsov, V Sinii, V Kurenkov, S Kolesnikov arXiv preprint arXiv:2406.08973, 2024 | 1 | 2024 |
| N-Gram Induction Heads for In-Context RL: Improving Stability and Reducing Data Needs I Zisman, A Nikulin, A Polubarov, N Lyubaykin, V Kurenkov arXiv preprint arXiv:2411.01958, 2024 | | 2024 |
| Mathematical modelling of tensegrity robots with rigid rods SI Savin, LI Vorochaeva, VV Kurenkov Computer research and modeling 12 (4), 821-830, 2020 | | 2020 |
Alexander Nikulindunnolab.aiVerified email at edu.hse.ru
