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Janarthanan Rajendran

janarthanan.rajendran@mila.quebec
Postdoctorat - Université de Montréal
Superviseur⋅e principal⋅e
Sarath Chandar Anbil Parthipan
Co-superviseur⋅e
Doina Precup
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Publications

Dealing With Non-stationarity in Decentralized Cooperative Multi-Agent Deep Reinforcement Learning via Multi-Timescale Learning
Hadi Nekoei
Akilesh Badrinaaraayanan
Amit Sinha
Mohammad Amin Amini
Janarthanan Rajendran
Aditya Mahajan
Sarath Chandar Anbil Parthipan
2023-02-06
ArXiv (prépublication)
doi.org
arxiv.org
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
Sarath Chandar Anbil Parthipan
2023-01-01
CoLLAs (publié)
proceedings.mlr.press
arxiv.org
Replay Buffer With Local Forgetting for Adaptive Deep Model-Based Reinforcement Learning
Ali Rahimi-Kalahroudi
Janarthanan Rajendran
Ida Momennejad
Harm van Seijen
Sarath Chandar Anbil Parthipan
One of the key behavioral characteristics used in neuroscience to determine whether the subject of study—be it a rodent or a human—exhib… (voir plus)its model-based learning is effective adaptation to local changes in the environment. In reinforcement learning, however, recent work has shown that modern deep model-based reinforcement-learning (MBRL) methods adapt poorly to such changes. An explanation for this mismatch is that MBRL methods are typically designed with sample-efficiency on a single task in mind and the requirements for effective adaptation are substantially higher, both in terms of the learned world model and the planning routine. One particularly challenging requirement is that the learned world model has to be sufficiently accurate throughout relevant parts of the state-space. This is challenging for deep-learning-based world models due to catastrophic forgetting. And while a replay buffer can mitigate the effects of catastrophic forgetting, the traditional first-in-first-out replay buffer precludes effective adaptation due to maintaining stale data. In this work
2022-12-09
NeurIPS.cc/2022/Workshop/DeepRL (inconnu)
doi.org
openreview.net
PatchBlender: A Motion Prior for Video Transformers
Gabriele Prato
Yale Song
Janarthanan Rajendran
(Rex) Devon Hjelm
Neel Joshi
Sarath Chandar Anbil Parthipan
2022-11-11
ArXiv (prépublication)
doi.org
openreview.net
Staged independent learning: Towards decentralized cooperative multi-agent Reinforcement Learning
Hadi Nekoei
Akilesh Badrinaaraayanan
Amit Sinha
Mohammad Amini
Janarthanan Rajendran
Aditya Mahajan
Sarath Chandar Anbil Parthipan
We empirically show that classic ideas from two-time scale stochastic approximation \citep{borkar1997stochastic} can be combined with sequen… (voir plus)tial iterative best response (SIBR) to solve complex cooperative multi-agent reinforcement learning (MARL) problems. We first start with giving a multi-agent estimation problem as a motivating example where SIBR converges while parallel iterative best response (PIBR) does not. Then we present a general implementation of staged multi-agent RL algorithms based on SIBR and multi-time scale stochastic approximation, and show that our new methods which we call Staged Independent Proximal Policy Optimization (SIPPO) and Staged Independent Q-learning (SIQL) outperform state-of-the-art independent learning on almost all the tasks in the epymarl \citep{papoudakis2020benchmarking} benchmark. This can be seen as a first step towards more decentralized MARL methods based on SIBR and multi-time scale learning.
2022-04-25
ICLR.cc/2022/Workshop/GMS (publié)
openreview.net
openreview.net