Membre des anciens élèves

Activité

  • Chercheur postdoctoral: juin 2021 - déc. 2023

Prépublications

Articles de conférence et de revue

2024

  1. Balancing Context Length and Mixing Times for Reinforcement Learning at Scale
    Matthew Riemer, Khimya Khetarpal, et
    Conference on Neural Information Processing Systems (NeurIPS), 2024.
    #RL
    [openreview]

  2. Toward Debugging Deep Reinforcement Learning Programs with RLExplorer
    Rached Bouchoucha, Ahmed Haj Yahmed, , , Amin Nikanjam, et Foutse Khomh
    International Conference on Software Maintenance and Evolution (ICSME), 2024.
    #RL
    [arXiv]

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

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

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

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
    [pmlr], [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
    [pmlr], [arXiv]

  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
    [pmlr], [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
    [pmlr], [arXiv]

2022

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