Here is a list of talks that I have given online (these are recorded):
1) Multi Type Mean Field Reinforcement Learning – A.I. Socratic Circles (AISC), Toronto (along with Matthew E. Taylor)
2) Multi Type Mean Field Reinforcement Learning – AAMAS 2020
3) Partially Observable Mean Field Reinforcement Learning – AAMAS 2021
4) Recent Advances in Mean-Field RL methods – University of Alberta – Alberta Machine Intelligence Institute (Amii) AI seminar – (Introduction by Matthew E. Taylor)
5) Decentralized Mean Field Games – AAAI 2022 (Preliminary version. Full conference version can be found here).
6) Decentralized Mean Field Games – University of Alberta – Alberta Machine Intelligence Institute (Amii) AI seminar – (Introduction by Matthew E. Taylor).
7) Multi-Agent Advisor Q-Learning – IJCAI 2023 (Please open link in Google Chrome).
8) Accelerating training in multi-agent reinforcement learning through action advising – U.C. Berkeley – Multi-Agent Learning Seminar.
Here is a list of talks online by co-authors on joint research works (these are recorded):
1) Extending Mean Field Reinforcement Learning to Partially Observable Environments, Agents of Multiple Types and Decentralized Learning: Machine Learning and Mean Field Games Seminar. – Talk by Pascal Poupart.
2) The Effect of Q-function Reuse on the Total Regret of Tabular, Model-Free, Reinforcement Learning: Adaptive and Learning Agents (ALA) workshop at AAMAS 2021. – Talk by Volodymr Tkachuk.
3) Deep Multi Agent Reinforcement Learning for Autonomous Driving: Canadian AI 2020 – Talk by Sushrut Bhalla.
4) Maximum Reward Formulation In Reinforcement Learning: NeurIPS Deep RL Workshop 2020 – Talk by Sai Krishna Gottipati.
Here is a list of talks I have given at various others points of time (not recorded):
1) Learning from multiple independent advisors in multi-agent reinforcement learning – AAMAS 2023
2) Multi-agent Reinforcement Learning in Large Complex Environments – Canadian AI 2023 (Doctoral Dessertation Award talk)
3) Deep Reinforcement Learning of Abstract Reasoning From Demonstrations – University of Waterloo.
4) Multi-Type Mean Field Reinforcement Learning – Conference of “Reinforcement Learning and Decision Making (RLDM)” – MCGill University, Montreal, Canada.
5) Learning Multi-Agent Communication with Reinforcement Learning – Conference of “Reinforcement Learning and Decision Making (RLDM)” – MCGill University, Montreal, Canada.
6) Algorithmic Analysis and Improvements in Multi-Agent Reinforcement Learning for Partially Observable Setting – University of Waterloo (AI seminar).
7) Wild Fire Response Using Game Theory and Reinforcement Learning – University of Waterloo.
8) Reinforcement Learning in SSP and Autonomous Driving domains – Borealis AI Toronto.
9) Reinforcement Learning for Determining Spread Dynamics of Spatially Spreading Processes with Emphasis on Forest Fires – MASc Seminar – University of Waterloo (MASc Seminar).
10) Learning Forest Wildfire Dynamics from Satellite Images Using Reinforcement Learning – Conference of “Reinforcement Learning and Decision Making (RLDM)” – University of Michgan, Ann Arbor, MI, USA.