Combining MCTS and A3C for Prediction of Spatially Spreading Processes in Forest Wildfire Settings. - Sriram Ganapathi Subramanian; Mark Crowley 001 (Canadian AI, 2018)

Decision Assist for Self-driving Cars. - Sriram Ganapathi Subramanian; Jaspreet Singh Sambee; Benyamin Ghojogh; Mark Crowley 001 (Canadian AI, 2018)

Using Spatial Reinforcement Learning to Build Forest Wildfire Dynamics Models From Satellite Images. - Sriram Ganapathi Subramanian; Mark Crowley 001 (Frontiers ICT, 2018)

Training Cooperative Agents for Multi-Agent Reinforcement Learning. - Sushrut Bhalla; Sriram Ganapathi Subramanian; Mark Crowley 001 (AAMAS, 2019)

Deep Multi Agent Reinforcement Learning for Autonomous Driving. - Sushrut Bhalla; Sriram Ganapathi Subramanian; Mark Crowley 001 (Canadian AI, 2020)

Multi Type Mean Field Reinforcement Learning. - Sriram Ganapathi Subramanian; Pascal Poupart; Matthew E. Taylor; Nidhi Hegde 001 (AAMAS, 2020)

A review of machine learning applications in wildfire science and management. - Piyush Jain; Sean C. P. Coogan; Sriram Ganapathi Subramanian; Mark Crowley 001; Steve Taylor; Mike D. Flannigan (CoRR, 2020)

Maximum Reward Formulation In Reinforcement Learning. - Sai Krishna Gottipati; Yashaswi Pathak; Rohan Nuttall; Sahir; Raviteja Chunduru; Ahmed Touati; Sriram Ganapathi Subramanian; Matthew E. Taylor; Sarath Chandar (CoRR, 2020)

Partially Observable Mean Field Reinforcement Learning. - Sriram Ganapathi Subramanian; Matthew E. Taylor; Mark Crowley 001; Pascal Poupart (AAMAS, 2021)

The Effect of Q-function Reuse on the Total Regret of Tabular, Model-Free, Reinforcement Learning. - Volodymyr Tkachuk; Sriram Ganapathi Subramanian; Matthew E. Taylor (CoRR, 2021)

Decentralized Mean Field Games. - Sriram Ganapathi Subramanian; Matthew E. Taylor; Mark Crowley 001; Pascal Poupart (AAAI, 2022)

Investigation of independent reinforcement learning algorithms in multi-agent environments. - Ken Ming Lee; Sriram Ganapathi Subramanian; Mark Crowley 001 (Frontiers Artif. Intell., 2022)

Multi-Agent Advisor Q-Learning. - Sriram Ganapathi Subramanian; Matthew E. Taylor; Kate Larson; Mark Crowley 001 (J. Artif. Intell. Res., 2022)

Learning from Multiple Independent Advisors in Multi-agent Reinforcement Learning. - Sriram Ganapathi Subramanian; Matthew E. Taylor; Kate Larson; Mark Crowley 001 (AAMAS, 2023)

Multi-Agent Advisor Q-Learning (Extended Abstract). - Sriram Ganapathi Subramanian; Matthew E. Taylor; Kate Larson; Mark Crowley 001 (IJCAI, 2023)

ChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry. - Chris Beeler; Sriram Ganapathi Subramanian; Kyle Sprague; Nouha Chatti; Colin Bellinger; Mitchell Shahen; Nicholas Paquin; Mark Baula; Amanuel Dawit; Zihan Yang; Xinkai Li; Mark Crowley 001; et al. (CoRR, 2023)

Two-Level Actor-Critic Using Multiple Teachers. - Su Zhang; Srijita Das 001; Sriram Ganapathi Subramanian; Matthew E. Taylor (Trans. Mach. Learn. Res., 2023)

Confidence Aware Inverse Constrained Reinforcement Learning. - Sriram Ganapathi Subramanian; Guiliang Liu; Mohammed Elmahgiubi; Kasra Rezaee; Pascal Poupart (ICML, 2024)

How Useful is Intermittent, Asynchronous Expert Feedback for Bayesian Optimization? - Agustinus Kristiadi; Felix Strieth-Kalthoff; Sriram Ganapathi Subramanian; Vincent Fortuin; Pascal Poupart; Geoff Pleiss (CoRR, 2024)

Learning to Negotiate via Voluntary Commitment. - Shuhui Zhu; Baoxiang Wang 001; Sriram Ganapathi Subramanian; Pascal Poupart (AISTATS, 2025)

A Comprehensive Survey on Inverse Constrained Reinforcement Learning: Definitions, Progress and Challenges. - Guiliang Liu; Sheng Xu; Shicheng Liu; Ashish Gaurav; Sriram Ganapathi Subramanian; Pascal Poupart (Trans. Mach. Learn. Res., 2025)

Simplifying Bayesian Optimization Via In-Context Direct Optimum Sampling. - Gustavo Sutter Pessurno de Carvalho; Mohammed Abdulrahman; Hao Wang; Sriram Ganapathi Subramanian; Marc St-Aubin; Sharon O'Sullivan; Lawrence Wan; Luis A. Ricardez-Sandoval; Pascal Poupart; Agustinus Kristiadi (CoRR, 2025)