Publications d'Janarthanan Rajendran

Activité

An Introduction to Lifelong Supervised Learning
Shagun Sodhani, Mojtaba Farmazi, Sanket Vaibhav Mehta, Pranshu Malviya, Mohamed Abdelsalam, Janarthanan Rajendran et Sarath Chandar
In ArXiv, 2022.
#DL
[arXiv]

Mastering Memory Tasks with World Models
Mohammad Reza Samsami*, Artem Zholus*, Janarthanan Rajendran et Sarath Chandar
International Conference on Learning Representations (ICLR), 2024. [Oral presentation.]
#RL, #DL
[openreview]
Intelligent Switching for Reset-Free RL
Darshan Patil, Janarthanan Rajendran, Glen Berseth et Sarath Chandar
International Conference on Learning Representations (ICLR), 2024.
#RL
[openreview]
Learning Conditional Policies for Crystal Design Using Offline Reinforcement Learning
Prashant Govindarajan, Santiago Miret, Jarrid Rector-Brooks, Mariano Phielipp, Janarthanan Rajendran et Sarath Chandar
Digital Discovery Journal, 2024.
#RL
[openreview]

Replay Buffer with Local Forgetting for Adapting to Local Environment Changes in Deep Model-Based Reinforcement Learning
Ali Rahimi-Kalahroudi, Janarthanan Rajendran, Ida Momennejad, Harm van Seijen et Sarath Chandar
Conference on Lifelong Learning Agents (CoLLAs), 2023.
[Deep Reinforcement Learning Workshop, NeurIPS, 2022]
#RL
[arXiv]
Towards Few-shot Coordination: Revisiting Ad-hoc Teamplay Challenge In the Game of Hanabi
Hadi Nekoei, Xutong Zhao, Janarthanan Rajendran, Miao Liu et Sarath Chandar
Conference on Lifelong Learning Agents (CoLLAs), 2023.
#RL
[paper]
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 et Sarath Chandar
Conference on Lifelong Learning Agents (CoLLAs), 2023.
#RL
[arXiv]
Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement Learning
Xutong Zhao, Yangchen Pan, Chenjun Xiao, Sarath Chandar et Janarthanan Rajendran
Conference on Uncertainty in Artificial Intelligence (UAI), 2023.
#RL
[arXiv]

Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods
Yi Wan*, Ali Rahimi-Kalahroudi*, Janarthanan Rajendran, Ida Momennejad, Sarath Chandar et Harm van Seijen
International Conference on Machine Learning (ICML), 2022.
#RL
[arXiv], [code]