| Resource constrained deep reinforcement learning A Bhatia, P Varakantham, A Kumar Proceedings of the International Conference on Automated Planning and …, 2019 | 49 | 2019 |
| Tuning the hyperparameters of anytime planning: A metareasoning approach with deep reinforcement learning A Bhatia, J Svegliato, SB Nashed, S Zilberstein Proceedings of the 32nd International Conference on Automated Planning and …, 2022 | 12 | 2022 |
| On the Benefits of Randomly Adjusting Anytime Weighted A* A Bhatia, J Svegliato, S Zilberstein Proceedings of the 14th Symposium on Combinatorial Search (SoCS), 2021 | 10 | 2021 |
| Tuning the Hyperparameters of Anytime Planning: A Deep Reinforcement Learning Approach A Bhatia, J Svegliato, S Zilberstein ICAPS 2021 Workshop on Heuristics and Search for Domain-independent Planning …, 2021 | 5 | 2021 |
| Selecting the partial state abstractions of MDPs: A metareasoning approach with deep reinforcement learning SB Nashed, J Svegliato, A Bhatia, S Russell, S Zilberstein IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022 | 4 | 2022 |
| Adaptive Rollout Length for Model-Based RL Using Model-Free Deep RL A Bhatia, PS Thomas, S Zilberstein arXiv preprint arXiv:2206.02380, 2022 | 3 | 2022 |
| RL^3: Boosting Meta Reinforcement Learning via RL inside RL^2 A Bhatia, SB Nashed, S Zilberstein NeurIPS 2023 Workshop on Generalization in Planning, 2023 | 2 | 2023 |
Shlomo ZilbersteinProfessor of Computer Science, University of Massachusetts Amherst在 cs.umass.edu 的电子邮件经过验证
