Beeler, C., Subramanian, S. G., Bellinger, C., Crowley, M., Tamblyn, I.. ChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry. Digital Discovery (To Appear), February 2024. Paper URL: https://arxiv.org/pdf/2305.14177.pdf
Beeler, C., Subramanian, S. G., Bellinger, C., Crowley, M., Tamblyn, I.. Demonstrating ChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry. AI for Accelerated Materials Design – NeurIPS 2023 Workshop. Paper URL: https://openreview.net/pdf?id=cSz69rFRvS
Beeler, C., Subramanian, S. G., Bellinger, C., Crowley, M., Tamblyn, I.. ChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry. AI for Science – NeurIPS 2023 Workshop. Paper URL: https://openreview.net/pdf?id=ZUkrNwMz5J
Zhang, S., Das, S., Subramanian, S. G., Taylor, M. E.. (2023). Two-Level Actor-Critic Using Multiple Teachers. In Transactions on Machine Learning Research (TMLR). Paper URL: https://openreview.net/pdf?id=LfQ6uAVAEo
Zhang, S., Das, S., Subramanian, S. G., Taylor, M. E.. (2023). Two-Level Actor-Critic Using Multiple Teachers. In International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2023), London, UK (Extended Abstract). Paper URL: https://www.southampton.ac.uk/~eg/AAMAS2023/pdfs/p2589.pdf
Subramanian, S. G., Taylor, M. E., Larson, K., & Crowley, M.. (2023). Learning from Multiple Independent Advisors in Multi-agent Reinforcement Learning. In International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2023), London, UK. Paper URL: https://arxiv.org/pdf/2301.11153.pdf
Subramanian, S. G., Taylor, M. E., Crowley, M., & Poupart, P.. (2022). Decentralized Mean Field Games. In AAAI Conference on Artificial Intelligence (2022), Vancouver, BC, Canada. AAAI press. Paper URL: https://arxiv.org/pdf/2112.09099.pdf
Subramanian, S. G., Taylor, M. E., Larson, K., & Crowley, M.. (2022). Multi-Agent Advisor Q-Learning. Journal of Aritificial Intelligence Research (JAIR), 74, 1–74. Paper URL: https://jair.org/index.php/jair/article/view/13445/26794
Lee, K. Ming, Subramanian, S. G., & Crowley, M.. (2021). Investigation of Independent Reinforcement Learning Algorithms in Multi-Agent Environments. In Frontiers in Artificial Intelligence, 5. Paper URL: https://www.frontiersin.org/articles/10.3389/frai.2022.805823/full
Tkachuk, V., Subramanian, S. G., & Taylor, M. E.. (2021). The Effect of Q-function Reuse on the Total Regret of Tabular, Model-Free, Reinforcement Learning. In International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2021), London, UK – Adaptive Learning Agents Workshop. Paper URL: https://arxiv.org/pdf/2103.04416.pdf
Subramanian, S. G., Taylor, M. E., Crowley, M., & Poupart, P.. (2021). Partially Observable Mean Field Reinforcement Learning. In International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2021), London, UK. Paper URL: https://arxiv.org/pdf/2012.15791.pdf
Gottipati, S. Krishna, Pathak, Y., Nuttall, R., Sahir,, Chunduru, R., Touati, A., Subramanian, S. G., et al. (2020). Maximum Reward Formulation In Reinforcement Learning. In Deep Reinforcement Learning Workshop. NeurIPS 2020. Paper URL: https://arxiv.org/pdf/2010.03744.pdf
Bhalla, S., Subramanian, S. G., & Crowley, M.. (2020). Deep Multi Agent Reinforcement Learning for Autonomous Driving. In Canadian AI . Springer LNCS. Paper URL: https://link.springer.com/chapter/10.1007/978-3-030-47358-7_7
Jain, P., Coogan, S. C. P., Subramanian, S. G., Crowley, M., Taylor, S., & Flannigan, M. D.. (2020). A review of machine learning applications in wildfire science and management. Environmental Reviews. Paper URL: https://arxiv.org/pdf/2003.00646.pdf
Subramanian, S. G., Poupart, P., Taylor, M. E., & Hegde, N.. (2020). Multi Type Mean Field Reinforcement Learning. In International Conference on Autonomous Agents and Multi agent Systems (AAMAS 2020), Aukland, New Zealand. IFAAMAS. Paper URL: https://arxiv.org/pdf/2002.02513.pdf
Bhalla, S., Subramanian, S. G., & Crowley, M.. (2019). Learning Multi-Agent Communication with Reinforcement Learning. In Conference on Reinforcement Learning and Decision Making. Paper URL: http://rldm.org/papers/abstracts.pdf
Bhalla, S., Subramanian, S. G., & Crowley, M.. (2019). Training Cooperative Agents for Multi-Agent Reinforcement Learning. In International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019), Montreal, Canada. Paper URL: http://www.ifaamas.org/Proceedings/aamas2019/pdfs/p1826.pdf
Subramanian, S. G., & Crowley, M.. (2018). A Complementary Approach to Improve WildFire Prediction Systems. In Neural Information Processing Systems (AI for social good workshop). Paper URL:https://aiforsocialgood.github.io/2018/pdfs/track1/37_aisg_neurips2018.pdf
Subramanian, S. G., Ghojogh, B., Sambee, J. S., & Crowley, M.. (2018). Decision Assist For Self-Driving Cars. In 31st Canadian Conference on Artificial Intelligence, Toronto (pp. 381 – 387). Springer. Paper URL: https://link.springer.com/chapter/10.1007/978-3-319-89656-4_44
Subramanian, S. G., & Crowley, M.. (2018). Combining MCTS and A3C for Prediction of Spatially Spreading Processes in Forest Wildfire Setting. In 31st Canadian Conference on Artificial Intelligence, Toronto (pp. 285-291). Springer. Paper URL: https://link.springer.com/chapter/10.1007/978-3-319-89656-4_28
Subramanian, S. G., & Crowley, M.. (2018). Using Spatial Reinforcement Learning to Build Forest Wildfire Dynamics Models from Satellite Images. Journal of Frontiers in ICT- Environmental Informatics. Paper URL: https://www.frontiersin.org/articles/10.3389/fict.2018.00006/full?&utm_source=Email_to_authors_&utm_medium=Email&utm_content=T1_11.5e1_author&utm_campaign=Email_publication&field=&journalName=Frontiers_in_ICT&id=334036
Subramanian, S. G., & Crowley, M.. (2017). Learning Forest Wildfire Dynamics from Satellite Images using Reinforcement Learning. In Conference on Reinforcement Learning and Decision Making (pp. 244-248). Paper URL: http://www.princeton.edu/~ndaw/RLDM17ExtendedAbstracts.pdf
Subramanian, S. G., & Ganesh, P.A.. (2015). Spatial Decision Support System for Industrial Robots. In Innovations in Marine Electrical and Electronics Engineering. Paper URL: https://uwaterloo.ca/scholar/sites/ca.scholar/files/s2ganapa/files/paper29.pdf