| State abstractions for lifelong reinforcement learning D Abel, D Arumugam, L Lehnert, M Littman International Conference on Machine Learning, 10-19, 2018 | 144 | 2018 |
| Deep reinforcement learning from policy-dependent human feedback D Arumugam, JK Lee, S Saskin, ML Littman arXiv preprint arXiv:1902.04257, 2019 | 97 | 2019 |
| Sequence-to-Sequence Language Grounding of Non-Markovian Task Specifications. N Gopalan, D Arumugam, LLS Wong, S Tellex Robotics: Science and Systems 2018, 2018 | 67 | 2018 |
| Accurately and efficiently interpreting human-robot instructions of varying granularities D Arumugam, S Karamcheti, N Gopalan, LLS Wong, S Tellex Robotics: Science and Systems, 2017 | 66 | 2017 |
| Grounding English commands to reward functions J MacGlashan, M Babes-Vroman, M desJardins, ML Littman, S Muresan, ... Robotics: Science and Systems, 2015 | 62* | 2015 |
| Value preserving state-action abstractions D Abel, N Umbanhowar, K Khetarpal, D Arumugam, D Precup, M Littman International Conference on Artificial Intelligence and Statistics, 1639-1650, 2020 | 61 | 2020 |
| State abstraction as compression in apprenticeship learning D Abel, D Arumugam, K Asadi, Y Jinnai, ML Littman, LLS Wong Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 3134-3142, 2019 | 57 | 2019 |
| Grounding natural language instructions to semantic goal representations for abstraction and generalization D Arumugam, S Karamcheti, N Gopalan, EC Williams, M Rhee, LLS Wong, ... Autonomous Robots 43, 449-468, 2019 | 30 | 2019 |
| An information-theoretic perspective on credit assignment in reinforcement learning D Arumugam, P Henderson, PL Bacon arXiv preprint arXiv:2103.06224, 2021 | 21 | 2021 |
| A tale of two draggns: A hybrid approach for interpreting action-oriented and goal-oriented instructions S Karamcheti, EC Williams, D Arumugam, M Rhee, N Gopalan, LLS Wong, ... arXiv preprint arXiv:1707.08668, 2017 | 21 | 2017 |
| Deciding what to learn: A rate-distortion approach D Arumugam, B Van Roy International Conference on Machine Learning, 373-382, 2021 | 17 | 2021 |
| 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 |
| Toward good abstractions for lifelong learning D Abel, D Arumugam, L Lehnert, ML Littman NIPS Workshop on Hierarchical Reinforcement Learning, 2017 | 13 | 2017 |
| Deciding what to model: Value-equivalent sampling for reinforcement learning D Arumugam, B Van Roy Advances in neural information processing systems 35, 9024-9044, 2022 | 11 | 2022 |
| The value of information when deciding what to learn D Arumugam, B Van Roy Advances in neural information processing systems 34, 9816-9827, 2021 | 11 | 2021 |
| Modeling latent attention within neural networks C Grimm, D Arumugam, S Karamcheti, D Abel, LLS Wong, ML Littman arXiv preprint arXiv:1706.00536, 2017 | 10* | 2017 |
| Interpreting human-robot instructions S Tellex, D Arumugam, S Karamcheti, N Gopalan, LLS Wong US Patent 10,606,898, 2020 | 9 | 2020 |
| Randomized value functions via posterior state-abstraction sampling D Arumugam, B Van Roy arXiv preprint arXiv:2010.02383, 2020 | 7 | 2020 |
| Social contract ai: Aligning ai assistants with implicit group norms JP Fränken, S Kwok, P Ye, K Gandhi, D Arumugam, J Moore, A Tamkin, ... arXiv preprint arXiv:2310.17769, 2023 | 6 | 2023 |
| Shattering the agent-environment interface for fine-tuning inclusive language models W Xu, S Dong, D Arumugam, B Van Roy arXiv preprint arXiv:2305.11455, 2023 | 6 | 2023 |
Michael LittmanBrown University在 brown.edu 的电子邮件经过验证
