Prépublications

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

Articles de conférence et de revue

2024

  1. Sub-goal Distillation: A Method to Improve Small Language Agents
    , Elias Stengel-Eskin, et Marc-Alexandre Cote
    Conference on Lifelong Learning Agents (CoLLAs), 2024.
    #RL, #NLP
    [arXiv]

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

  3. Mastering Memory Tasks with World Models
    , , et
    International Conference on Learning Representations (ICLR), 2024. [Oral presentation.]
    #RL, #DL
    [openreview]

  4. Intelligent Switching for Reset-Free RL
    , , Glen Berseth et
    International Conference on Learning Representations (ICLR), 2024.
    #RL
    [openreview]

  5. Learning Conditional Policies for Crystal Design Using Offline Reinforcement Learning
    , Santiago Miret, , Mariano Phielipp, et
    Digital Discovery Journal, 2024.
    #RL
    [openreview]

2023

  1. Replay Buffer with Local Forgetting for Adapting to Local Environment Changes in Deep Model-Based Reinforcement Learning
    , , Ida Momennejad, Harm van Seijen et
    Conference on Lifelong Learning Agents (CoLLAs), 2023.
    [Deep Reinforcement Learning Workshop, NeurIPS, 2022]
    #RL
    [arXiv]

  2. Towards Few-shot Coordination: Revisiting Ad-hoc Teamplay Challenge In the Game of Hanabi
    , , , Miao Liu et
    Conference on Lifelong Learning Agents (CoLLAs), 2023.
    #RL
    [paper]

  3. Dealing With Non-stationarity in Decentralized Cooperative Multi-Agent Deep Reinforcement Learning via Multi-Timescale Learning
    , , Amit Sinha, Mohammad Amini, , Aditya Mahajan et
    Conference on Lifelong Learning Agents (CoLLAs), 2023.
    #RL
    [arXiv]

  4. Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement Learning
    , Yangchen Pan, Chenjun Xiao, et
    Conference on Uncertainty in Artificial Intelligence (UAI), 2023.
    #RL
    [arXiv]

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

2022

  1. Combining Reinforcement Learning and Constraint Programming for Sequence-Generation Tasks with Hard Constraints
    , et Gilles Pesant
    Principles and Practice of Constraint Programming (CP), 2022.
    #RL

  2. Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods
    , , , Ida Momennejad, et Harm van Seijen
    International Conference on Machine Learning (ICML), 2022.
    #RL
    [arXiv], [code]

2021

  1. Continuous Coordination As a Realistic Scenario for Lifelong Learning
    , , Aaron Courville et
    International Conference on Machine Learning (ICML), 2021.
    #RL
    [arXiv], [code]

  2. 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 et
    AAAI Conference on Artificial Intelligence (AAAI), 2021.
    #RL

2020

  1. The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning
    Harm van Seijen, , et
    Neural Information Processing Systems (NeurIPS), 2020.
    #RL
    [arXiv], [code]

  2. 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, et Yoshua Bengio
    International Conference on Machine Learning (ICML), 2020.
    #RL
    [arXiv]