| A review of machine learning applications in wildfire science and management P Jain, SCP Coogan, SG Subramanian, M Crowley, S Taylor, ... Environmental Reviews 28 (4), 478-505, 2020 | 566 | 2020 |
| Multi type mean field reinforcement learning SG Subramanian, P Poupart, ME Taylor, N Hegde Proceedings of the 2020 International Conference on Autonomous Agents and …, 2020 | 69 | 2020 |
| Deep multi agent reinforcement learning for autonomous driving S Bhalla, S Ganapathi Subramanian, M Crowley Canadian Conference on Artificial Intelligence, 67-78, 2020 | 67 | 2020 |
| Using spatial reinforcement learning to build forest wildfire dynamics models from satellite images S Ganapathi Subramanian, M Crowley Frontiers in ICT 5, 6, 2018 | 60 | 2018 |
| Calendar graph neural networks for modeling time structures in spatiotemporal user behaviors D Wang, M Jiang, M Syed, O Conway, V Juneja, S Subramanian, ... Proceedings of the 26th ACM SIGKDD international conference on knowledge …, 2020 | 48 | 2020 |
| Partially observable mean field reinforcement learning SG Subramanian, ME Taylor, M Crowley, P Poupart Proceedings of the 2021 International Conference on Autonomous Agents and …, 2020 | 30 | 2020 |
| Learning forest wildfire dynamics from satellite images using reinforcement learning SG Subramanian, M Crowley Conference on reinforcement learning and decision making, 2017 | 26 | 2017 |
| Decentralized mean field games SG Subramanian, ME Taylor, M Crowley, P Poupart Proceedings of the AAAI Conference on Artificial Intelligence 36 (9), 9439-9447, 2022 | 17 | 2022 |
| Investigating TCP performance issues in satellite networks S Subramanian, S Sivakumar, WJ Phillips, W Robertson 3rd Annual Communication Networks and Services Research Conference (CNSR'05 …, 2005 | 16 | 2005 |
| Combining MCTS and A3C for prediction of spatially spreading processes in forest wildfire settings S Ganapathi Subramanian, M Crowley Advances in Artificial Intelligence: 31st Canadian Conference on Artificial …, 2018 | 15 | 2018 |
| A deep reinforcement learning chatbot. arXiv 2017 IV Serban, C Sankar, M Germain, S Zhang, Z Lin, S Subramanian, T Kim, ... arXiv preprint arXiv:1709.02349, 2023 | 14 | 2023 |
| Maximum reward formulation in reinforcement learning SK Gottipati, Y Pathak, R Nuttall, R Chunduru, A Touati, SG Subramanian, ... arXiv preprint arXiv:2010.03744, 2020 | 14 | 2020 |
| Multi-agent advisor Q-learning SG Subramanian, ME Taylor, K Larson, M Crowley Journal of Artificial Intelligence Research 74, 1-74, 2022 | 12 | 2022 |
| Investigation of independent reinforcement learning algorithms in multi-agent environments KM Lee, S Ganapathi Subramanian, M Crowley Frontiers in Artificial Intelligence 5, 805823, 2022 | 11 | 2022 |
| Chemgymrl: An interactive framework for reinforcement learning for digital chemistry C Beeler, SG Subramanian, K Sprague, N Chatti, C Bellinger, M Shahen, ... arXiv preprint arXiv:2305.14177, 2023 | 7 | 2023 |
| Training Cooperative Agents for Multi-Agent Reinforcement Learning. S Bhalla, SG Subramanian, M Crowley AAMAS, 1826-1828, 2019 | 6 | 2019 |
| A review of machine learning applications in wildfire science and management. arXiv 2020 P Jain, SCP Coogan, SG Subramanian, M Crowley, S Taylor, ... arXiv preprint arXiv:2003.00646, 0 | 6 | |
| Learning from Multiple Independent Advisors in Multi-agent Reinforcement Learning SG Subramanian, ME Taylor, K Larson, M Crowley arXiv preprint arXiv:2301.11153, 2023 | 4 | 2023 |
| System and method for multi-type mean field reinforcement machine learning SG Subramanian, P Poupart, ME Taylor, N Hegde US Patent App. 16/804,593, 2020 | 4 | 2020 |
| Confidence aware inverse constrained reinforcement learning SG Subramanian, G Liu, M Elmahgiubi, K Rezaee, P Poupart arXiv preprint arXiv:2406.16782, 2024 | 2 | 2024 |