| State abstractions for lifelong reinforcement learning D Abel, D Arumugam, L Lehnert, M Littman International Conference on Machine Learning, 10-19, 2018 | 134 | 2018 |
| Advantages and limitations of using successor features for transfer in reinforcement learning L Lehnert, S Tellex, ML Littman arXiv preprint arXiv:1708.00102, 2017 | 51 | 2017 |
| Successor features combine elements of model-free and model-based reinforcement learning L Lehnert, ML Littman Journal of Machine Learning Research 21 (196), 1-53, 2020 | 36 | 2020 |
| Reward-predictive representations generalize across tasks in reinforcement learning L Lehnert, ML Littman, MJ Frank PLoS computational biology 16 (10), e1008317, 2020 | 34 | 2020 |
| On value function representation of long horizon problems L Lehnert, R Laroche, H van Seijen Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 25 | 2018 |
| Successor features support model-based and model-free reinforcement learning L Lehnert, ML Littman CoRR abs/1901.11437, 2019 | 14 | 2019 |
| Mitigating planner overfitting in model-based reinforcement learning D Arumugam, D Abel, K Asadi, N Gopalan, C Grimm, JK Lee, L Lehnert, ... arXiv preprint arXiv:1812.01129, 2018 | 13 | 2018 |
| Transfer with model features in reinforcement learning L Lehnert, ML Littman arXiv preprint arXiv:1807.01736, 2018 | 12 | 2018 |
| Toward good abstractions for lifelong learning D Abel, D Arumugam, L Lehnert, ML Littman NIPS Workshop on Hierarchical Reinforcement Learning, 2017 | 12 | 2017 |
| Policy gradient methods for off-policy control L Lehnert, D Precup arXiv preprint arXiv:1512.04105, 2015 | 7 | 2015 |
| Iql-td-mpc: Implicit q-learning for hierarchical model predictive control R Chitnis, Y Xu, B Hashemi, L Lehnert, U Dogan, Z Zhu, O Delalleau arXiv preprint arXiv:2306.00867, 2023 | 5 | 2023 |
| Beyond A*: Better Planning with Transformers via Search Dynamics Bootstrapping L Lehnert, S Sukhbaatar, P Mcvay, M Rabbat, Y Tian arXiv preprint arXiv:2402.14083, 2024 | 4 | 2024 |
| Maximum State Entropy Exploration using Predecessor and Successor Representations AK Jain, L Lehnert, I Rish, G Berseth Advances in Neural Information Processing Systems 36, 2024 | | 2024 |
| IQL-TD-MPC: Implicit Q-Learning for Hierarchical Model Predictive Control Y Xu, R Chitnis, BT Hashemi, L Lehnert, U Dogan, Z Zhu, O Delalleau ICML Workshop on New Frontiers in Learning, Control, and Dynamical Systems, 2023 | | 2023 |
| Reward-Predictive Clustering L Lehnert, MJ Frank, ML Littman arXiv preprint arXiv:2211.03281, 2022 | | 2022 |
| Off-policy control under changing behaviour L Lehnert McGill University (Canada), 2016 | | 2016 |
| Connection forms for beating the heart: LV mechanics challenge (methods) A Mensch, E Piuze, L Lehnert, AJ Bakermans, J Sporring, GJ Strijkers, ... Statistical Atlases and Computational Models of the Heart-Imaging and …, 2015 | | 2015 |
| Beyond A*: Better LLM planning via Search Dynamics Bootstrapping L Lehnert, S Sukhbaatar, P McVay, M Rabbat, Y Tian ICLR 2024 Workshop on Large Language Model (LLM) Agents, 0 | | |
| Using Policy Gradients to Account for Changes in Behaviour Policies under Off-policy Control L Lehnert, D Precup | | |
| David Abel D Abel, EC Williams, S Brawner, E Reif, ML Littman, DE Hershkowitz, ... | | |
