Publications | Reinforcement Learning

Preprints

• BindGPT: A Scalable Framework for 3D Molecular Design via Language Modeling and Reinforcement Learning
  Artem Zholus, Maksim Kuznetsov, Roman Schutski, Rim Shayakhmetov, Daniil Polykovskiy, Sarath Chandar, and Alex Zhavoronkov
  In arXiv, 2024.
  #DL, #RL
  [arXiv], [website]

• Maximum Reward Formulation In Reinforcement Learning
  Sai Krishna Gottipati, Yashaswi Pathak, Rohan Nuttall, Raviteja Chunduru, Ahmed Touati, Sriram Ganapathi Subramanian, Matthew E Taylor, and Sarath Chandar
  In arXiv, 2020.
  #RL
  [arXiv]

Conference and Journal Papers

2024

18. Balancing Context Length and Mixing Times for Reinforcement Learning at Scale
    Matthew Riemer, Khimya Khetarpal, Janarthanan Rajendran, and Sarath Chandar
    Neural Information Processing Systems (NeurIPS), 2024.
    #RL
    

19. Toward Debugging Deep Reinforcement Learning Programs with RLExplorer
    Rached Bouchoucha, Ahmed Haj Yahmed, Darshan Patil, Janarthanan Rajendran, Amin Nikanjam, Sarath Chandar, and Foutse Khomh
    International Conference on Software Maintenance and Evolution (ICSME), 2024.
    #RL
    

20. Sub-goal Distillation: A Method to Improve Small Language Agents
    Maryam Hashemzadeh, Elias Stengel-Eskin, Sarath Chandar, and Marc-Alexandre Cote
    Conference on Lifelong Learning Agents (CoLLAs), 2024. [Oral presentation.]
    #RL, #NLP
    [arXiv]

21. Partial Models for Building Adaptive Model-Based Reinforcement Learning Agents
    Safa Alver, Ali Rahimi-Kalahroudi, and Doina Precup
    Conference on Lifelong Learning Agents (CoLLAs), 2024.
    #RL
    [arXiv]

22. Mastering Memory Tasks with World Models
    Mohammad Reza Samsami*, Artem Zholus*, Janarthanan Rajendran, and Sarath Chandar
    International Conference on Learning Representations (ICLR), 2024. [Oral presentation.]
    #RL, #DL
    [openreview]

23. Intelligent Switching for Reset-Free RL
    Darshan Patil, Janarthanan Rajendran, Glen Berseth, and Sarath Chandar
    International Conference on Learning Representations (ICLR), 2024.
    #RL
    [openreview]

24. Learning Conditional Policies for Crystal Design Using Offline Reinforcement Learning
    Prashant Govindarajan, Santiago Miret, Jarrid Rector-Brooks, Mariano Phielipp, Janarthanan Rajendran, and Sarath Chandar
    Digital Discovery Journal, 2024.
    #RL
    [openreview]

2023

11. 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, and Sarath Chandar
    Conference on Lifelong Learning Agents (CoLLAs), 2023.
    [Deep Reinforcement Learning Workshop, NeurIPS, 2022]
    #RL
    [arXiv]

12. Towards Few-shot Coordination: Revisiting Ad-hoc Teamplay Challenge In the Game of Hanabi
    Hadi Nekoei, Xutong Zhao, Janarthanan Rajendran, Miao Liu, and Sarath Chandar
    Conference on Lifelong Learning Agents (CoLLAs), 2023.
    #RL
    [paper]

13. 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, and Sarath Chandar
    Conference on Lifelong Learning Agents (CoLLAs), 2023.
    #RL
    [arXiv]

14. Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement Learning
    Xutong Zhao, Yangchen Pan, Chenjun Xiao, Sarath Chandar, and Janarthanan Rajendran
    Conference on Uncertainty in Artificial Intelligence (UAI), 2023.
    #RL
    [arXiv]

15. Multi-Agent Reinforcement Learning for Fast-Timescale Demand Response of Residential Loads
    Vincent Mai, Philippe Maisonneuve, Tianyu Zhang, Hadi Nekoei, Liam Paull, and Antoine Lesage-Landry
    International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2023.
    #RL
    [arXiv]

2022

6. Combining Reinforcement Learning and Constraint Programming for Sequence-Generation Tasks with Hard Constraints
   Daphné Lafleur, Sarath Chandar, and Gilles Pesant
   Principles and Practice of Constraint Programming (CP), 2022.
   #RL
   

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

2021

4. Continuous Coordination As a Realistic Scenario for Lifelong Learning
   Hadi Nekoei, Akilesh Badrinaaraayanan, Aaron Courville, and Sarath Chandar
   International Conference on Machine Learning (ICML), 2021.
   #RL
   [arXiv], [code]

5. Towered Actor Critic for Handling Multiple Action Types in Reinforcement Learning For Drug Discovery
   Sai Krishna Gottipati, Yashaswi Pathak, Boris Sattarov, Sahir, Rohan Nuttall, Mohammad Amini, Matthew E. Taylor, and Sarath Chandar
   AAAI Conference on Artificial Intelligence (AAAI), 2021.
   #RL
   

2020

2. The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning
   Harm van Seijen, Hadi Nekoei, Evan Racah, and Sarath Chandar
   Neural Information Processing Systems (NeurIPS), 2020.
   #RL
   [arXiv], [code]

3. Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning
   Sai Krishna Gottipati*, Boris Sattarov*, Sufeng Niu, Yashaswi Pathak, Haoran Wei, Shengchao Liu, Karam MJ Thomas, Simon Blackburn, Connor W Coley, Jian Tang, Sarath Chandar, and Yoshua Bengio
   International Conference on Machine Learning (ICML), 2020.
   #RL
   [arXiv]