ஜனார்த்தனன் ராஜேந்திரன்
Janarthanan Rajendran (he/him)
வணக்கம் (~ Greetings in Tamil) :)
I am currently the Sexton Chair in Reinforcement Learning and an Assistant Professor at the Faculty of Computer Science at Dalhousie University in Halifax, Mi'kma'ki, Canada.
My research interests lie in building systems that through interaction can learn to be competent in complex, dynamic, and uncertain environments. I am interested in the computational methods to build such systems as well as their practical applications and societal implications. To this end, my research focuses on deep reinforcement learning.
If you are interested in working with me at Dalhousie University, please refer to the Prospective Students page.
Prior to my current position, I was an IVADO Postdoctoral Research Fellow at Mila - Quebec Artificial Intelligence Institute and the University of Montréal, working with Prof. Sarath Chandar and Prof. Doina Precup. I completed my Ph.D. in Computer Science and Engineering (stream: Artificial Intelligence) at the University of Michigan, Ann Arbor, working with Prof. Satinder Singh, and completed my M.Tech. and B.Tech. in Electrical Engineering (major stream: Communication Engineering, minor stream: Physics) at the Indian Institute of Technology Madras, working with Prof. Balaraman Ravindran and Prof. Kaushik Mitra.
E-Mail: (janarthanan.rajendran@dal.ca) | LinkedIn | Twitter | Google Scholar | Dalhousie University
Latest News
May 2024: Join us at the inaugural Dalhousie AI Symposium on May 9th!
Apr 2024: I am happy to share that I have been awarded the 'Sexton Chair in Reinforcement Learning'.:) It is a research chair in the Faculty of Computer Science at Dalhousie University, awarded to emerging researchers having the potential to lead in their field.
Apr 2024: Our work Dynamic Incentives in Response to Dynamic Pricing, Jesse Thibodeau, Hadi Nekoei, Afaf Taïk, Janarthanan Rajendran, Golnoosh Farnadi, will be presented at the Econometric Society Interdisciplinary Frontiers (ESIF) conference on Economics and AI+ML 2024.
Jan 2024: Our work Mastering Memory Tasks with World Models, Mohammad Reza Samsami, Artem Zholus, Janarthanan Rajendran, Sarath Chandar, has been accepted for an oral presentation at ICLR 2024. :)
Jan 2024: Our work Intelligent Switching for Reset-Free RL, Darshan Patil, Janarthanan Rajendran, Glen Berseth, Sarath Chandar, has been accepted at ICLR 2024. :)
Jan 2024: Our work Learning Conditional Policies for Crystal Design Using Offline Reinforcement Learning, Prashant Govindarajan, Santiago Miret, Jarrid Rector-Brooks, Mariano Phielipp, Janarthanan Rajendran, Sarath Chandar has been accepted for the AI for Accelerated Materials Design (AI4Mat) 2023 Digital Discovery Journal Special Issue. :)
Jan 2024: Started as an Assistant Professor at the Faculty of Computer Science, Dalhousie University. :)
Nov 2023: Our work Mastering Memory Tasks with World Models, Mohammad Reza Samsami, Artem Zholus, Janarthanan Rajendran, Sarath Chandar, will be presented at the Workshop on Agent Learning in Open-Endedness (ALOE), NeurIPS 2023.
Nov 2023: Our work Learning Conditional Policies for Crystal Design Using Offline Reinforcement Learning, Prashant Govindarajan, Santiago Miret, Jarrid Rector-Brooks, Mariano Phielipp, Janarthanan Rajendran, Sarath Chandar, will be presented at the Workshop on AI for Accelerated Materials Design (AI4Mat), NeurIPS 2023.
July 2023: Our work Dealing With Non-stationarity in Decentralized Cooperative Multi-Agent Deep Reinforcement Learning via Multi-Timescale Learning, Hadi Nekoei, Akilesh Badrinaaraayanan, Amit Sinha, Mohammad Amini, Janarthanan Rajendran, Aditya Mahajan, Sarath Chandar, has been accepted at the Conference on Lifelong Learning Agents (CoLLAs) 2023.
June 2023: I will be joining the Faculty of Computer Science at Dalhousie University, Halifax, as an Assistant Professor in January 2024. :)
Archived News
May 2023: Our work Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement Learning, Xutong Zhao, Yangchen Pan, Chenjun Xiao, Sarath Chandar, Janarthanan Rajendran, has been accepted at the Conference on Uncertainty in Artificial Intelligence (UAI) 2023.
May 2023: Our work Replay Buffer with Local Forgetting for Adaptive Deep Model-Based Reinforcement Learning, Ali Rahimi-Kalahroudi, Janarthanan Rajendran, Ida Momennejad, Harm van Seijen, Sarath Chandar, has been accepted at the Conference on Lifelong Learning Agents (CoLLAs) 2023.
May 2023: Our work Towards Few-shot Coordination: Revisiting Ad-Hoc Teamplay Challenge in the Game of Hanabi, Hadi Nekoei, Xutong Zhao, Janarthanan Rajendran, Miao Liu, Sarath Chandar, has been accepted at the Conference on Lifelong Learning Agents (CoLLAs) 2023.
March 2023: Our work Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement Learning, Xutong Zhao, Yangchen Pan, Chenjun Xiao, Sarath Chandar, Janarthanan Rajendran, is now up on arXiv.
Feb 2023: Our work Dealing With Non-stationarity in Decentralized Cooperative Multi-Agent Deep Reinforcement Learning via Multi-Timescale Learning, Hadi Nekoei, Akilesh Badrinaaraayanan, Amit Sinha, Mohammad Amini, Janarthanan Rajendran, Aditya Mahajan, Sarath Chandar, is now up on arXiv.
Nov 2022: Our work Replay Buffer with Local Forgetting for Adaptive Deep Model-Based Reinforcement Learning, Ali Rahimi-Kalahroudi, Janarthanan Rajendran, Ida Momennejad, Harm van Seijen, Sarath Chandar, has been accepted at the Workshop on Deep Reinforcement Learning at NeurIPS 2022.
Oct 2022: Our work PatchBlender: A Motion Prior for Video Transformers, Gabriele Prato, Yale Song, Janarthanan Rajendran, Devon Hjelm, Neel Joshi, Sarath Chandar, has been accepted at Workshop on Vision Transformers: Theory and Applications at NeurIPS 2022.
Aug 2022: Conference on Lifelong Learning Agents (CoLLAs) 2022!
July 2022: Our work An Introduction to Lifelong Supervised Learning, Shagun Sodhani, Mojtaba Faramarzi, Sanket Vaibhav Mehta, Pranshu Malviya, Mohamed Abdelsalam, Janarthanan Janarthanan, Sarath Chandar, is now up on arXiv.
May 2022: Our work Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods, Yi Wan*, Ali Rahimi-Kalahroudi*, Janarthanan Rajendran, Ida Momennejad, Sarath Chandar, Harm van Seijen, has been accepted at the International Conference on Machine Learning (ICML) 2022.
Mar 2022: Our work Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods, Yi Wan*, Ali Rahimi-Kalahroudi*, Janarthanan Rajendran, Ida Momennejad, Sarath Chandar, Harm van Seijen, has been accepted at the Workshop on Agent Learning in Open-Endedness (ALOE) at ICRL 2022.
Mar 2022: Our work Staged independent learning: Towards decentralized cooperative multi-agent Reinforcement Learning, Hadi Nekoei, Akilesh Badrinaaraayanan, Amit Sinha, Mohammad Amini, Janarthanan Rajendran, Aditya Mahajan, Sarath Chandar, has been accepted at the Workshop on Gamification and Multi-Agent Solutions at ICLR 2022.