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 | 80 | 2022 |
Robustness and sample complexity of model-based MARL for general-sum Markov games J Subramanian, A Sinha, A Mahajan Dynamic Games and Applications 13 (1), 56-88, 2023 | 10 | 2023 |
Dealing with non-stationarity in decentralized cooperative multi-agent deep reinforcement learning via multi-timescale learning H Nekoei, A Badrinaaraayanan, A Sinha, M Amini, J Rajendran, ... Conference on Lifelong Learning Agents, 376-398, 2023 | 8* | 2023 |
An approach towards automated navigation of vehicles using overhead cameras VSC Kumar, A Sinha, PP Mallya, N Nath 2017 IEEE International Conference on Computational Intelligence and …, 2017 | 6 | 2017 |
Approximate information state based convergence analysis of recurrent Q-learning E Seyedsalehi, N Akbarzadeh, A Sinha, A Mahajan arXiv preprint arXiv:2306.05991, 2023 | 4 | 2023 |
Robustness of Whittle index policy to model approximation A Sinha, A Mahajan Available at SSRN 4064507, 2022 | 4 | 2022 |
Efficient Face Detection Using Neural Networks A Sinha International Conference on Computational Intelligence, 279-285, 2015 | 1 | 2015 |
Asymmetric actor-critic with approximate information state A Sinha, A Mahajan 2023 62nd IEEE Conference on Decision and Control (CDC), 7810-7816, 2023 | | 2023 |
Reinforcement learning in partially observable environments using approximate information state A Sinha McGill University, 2021 | | 2021 |