Programmatically interpretable reinforcement learning A Verma, V Murali, R Singh, P Kohli, S Chaudhuri Thirty-fifth International Conference on Machine Learning (ICML), 2018 | 415 | 2018 |
Imitation-projected programmatic reinforcement learning A Verma, H Le, Y Yue, S Chaudhuri Advances in Neural Information Processing Systems (NeurIPS), 15752-15763, 2019 | 100 | 2019 |
Control regularization for reduced variance reinforcement learning R Cheng, A Verma, G Orosz, S Chaudhuri, Y Yue, JW Burdick Thirty-sixth International Conference on Machine Learning (ICML), 2019 | 89 | 2019 |
Neurosymbolic Reinforcement Learning with Formally Verified Exploration G Anderson, A Verma, I Dillig, S Chaudhuri Advances in Neural Information Processing Systems (NeurIPS) 33, 2020 | 82 | 2020 |
Learning Differentiable Programs with Admissible Neural Heuristics A Shah, E Zhan, J Sun, A Verma, Y Yue, S Chaudhuri Advances in Neural Information Processing Systems (NeurIPS) 33, 2020 | 51 | 2020 |
Representing formal languages: A comparison between finite automata and recurrent neural networks JJ Michalenko, A Shah, A Verma, RG Baraniuk, S Chaudhuri, AB Patel International Conference on Learning Representations (ICLR), 2019 | 30 | 2019 |
Verifiable and interpretable reinforcement learning through program synthesis A Verma Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 9902-9903, 2019 | 6 | 2019 |
Compositional policy learning in stochastic control systems with formal guarantees Đ Žikelić, M Lechner, A Verma, K Chatterjee, T Henzinger Advances in Neural Information Processing Systems 36, 2024 | 4 | 2024 |
Eventual discounting temporal logic counterfactual experience replay C Voloshin, A Verma, Y Yue International Conference on Machine Learning, 35137-35150, 2023 | 3 | 2023 |
Deep Policy Optimization with Temporal Logic Constraints A Shah, C Voloshin, C Yang, A Verma, S Chaudhuri, SA Seshia arXiv preprint arXiv:2404.11578, 2024 | 1 | 2024 |
Programmatic reinforcement learning A Verma | | 2021 |