Reinforcement Learning in Stationary Mean-field Games J Subramanian, A Mahajan Proceedings of the 18th International Conference on Autonomous Agents and …, 2019 | 64 | 2019 |
Public health impact of delaying second dose of BNT162b2 or mRNA-1273 covid-19 vaccine: simulation agent based modeling study S Romero-Brufau, A Chopra, AJ Ryu, E Gel, R Raskar, W Kremers, ... bmj 373, 2021 | 42 | 2021 |
On the link between weighted least-squares and limiters used in higher-order reconstructions for finite volume computations of hyperbolic equations JC Mandal, J Subramanian Applied Numerical Mathematics 58 (5), 705-725, 2008 | 38 | 2008 |
Approximate information state for approximate planning and reinforcement learning in partially observed systems J Subramanian, A Sinha, R Seraj, A Mahajan Journal of Machine Learning Research 23 (12), 1-83, 2022 | 20 | 2022 |
Approximate information state for partially observed systems J Subramanian, A Mahajan Reinforcement Learning and Decision Making (RLDM), Montreal, July 7-10, 2019, 2019 | 18 | 2019 |
An empirical study of representation learning for reinforcement learning in healthcare TW Killian, H Zhang, J Subramanian, M Fatemi, M Ghassemi arXiv preprint arXiv:2011.11235, 2020 | 9 | 2020 |
Renewal Monte Carlo: Renewal theory based reinforcement learning J Subramanian, A Mahajan Proceedings of the IEEE Conference on Decision and Control (CDC), Miami, Florida, 2018 | 8 | 2018 |
Transient aero-thermal mapping of passive Thermal Protection system for nose-cap of Reusable Hypersonic Vehicle SP Mahulikar, S Khurana, R Dungarwal, SG Shevakari, J Subramanian, ... The Journal of the Astronautical Sciences 56 (4), 593-619, 2008 | 8 | 2008 |
Stochastic approximation based methods for computing the optimal thresholds in remote-state estimation with packet drops J Chakravorty, J Subramanian, A Mahajan American Control Conference (ACC), 2017, 462-467, 2017 | 7 | 2017 |
High‐resolution finite volume computations using a novel weighted least‐squares formulation JC Mandal, S Rao, J Subramanian International journal for numerical methods in fluids 56 (8), 1425-1431, 2008 | 5 | 2008 |
Reinforcement learning for mean-field teams J Subramanian, R Seraj, A Mahajan Reinforcement Learning and Decision Making (RLDM), Montreal, July 7-10, 2019, 2019 | 4 | 2019 |
Reinforcement Learning for Mean-field Teams J Subramanian, R Seraj, A Mahajan AAMAS Workshop on Adaptive and Learning Agents, 2019 | 4 | 2019 |
Status-quo policy gradient in Multi-Agent Reinforcement Learning P Badjatiya, M Sarkar, N Puri, J Subramanian, A Sinha, S Singh, ... arXiv preprint arXiv:2111.11692, 2021 | 1 | 2021 |
DeepABM: Scalable, efficient and differentiable agent-based simulations via graph neural networks A Chopra, E Gel, J Subramanian, B Krishnamurthy, S Romero-Brufau, ... arXiv preprint arXiv:2110.04421, 2021 | 1 | 2021 |
Robustness and sample complexity of model-based MARL for general-sum Markov games J Subramanian, A Sinha, A Mahajan arXiv preprint arXiv:2110.02355, 2021 | 1 | 2021 |
Inducing Cooperative behaviour in Sequential-Social dilemmas through Multi-Agent Reinforcement Learning using Status-Quo Loss P Badjatiya, M Sarkar, A Sinha, S Singh, N Puri, J Subramanian, ... arXiv preprint arXiv:2001.05458, 2020 | 1 | 2020 |
On controllability of leader-follower dynamics over a directed graph J Subramanian, A Mahajan Proceedings of the IEEE Conference on Decision and Control (CDC), Miami, Florida, 2018 | 1 | 2018 |
Providing insights and suggestions for journeys P Singhai, P Gupta, B Krishnamurthy, J SUBRAMANIAN, N Puri US Patent App. 16/910,357, 2021 | | 2021 |
Robustness of Markov perfect equilibrium to model approximations in general-sum dynamic games J Subramanian, A Sinha, A Mahajan 2021 Seventh Indian Control Conference (ICC), 189-194, 2021 | | 2021 |
DeepABM: Scalable and Efficient Agent-Based Simulations Via Geometric Learning Frameworks-a Case Study For Covid-19 Spread and Interventions A Chopra, R Raskar, J Subramanian, B Krishnamurthy, ES Gel, ... 2021 Winter Simulation Conference (WSC), 1-12, 2021 | | 2021 |