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Sahand Rezaei-Shoshtari

sahand.rezaei-shoshtari@mila.quebec
Doctorat - McGill University
Superviseur⋅e principal⋅e
Doina Precup
Co-superviseur⋅e
David Meger
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Publications

Policy Gradient Methods in the Presence of Symmetries and State Abstractions
Prakash Panangaden
Sahand Rezaei-Shoshtari
Rosie Zhao
David Meger
Doina Precup
arxiv.org
Hypernetworks for Zero-shot Transfer in Reinforcement Learning
Sahand Rezaei-Shoshtari
Charlotte Morissette
Francois Hogan
Gregory Dudek
David Meger
In this paper, hypernetworks are trained to generate behaviors across a range of unseen task conditions, via a novel TD-based training objec… (voir plus)tive and data from a set of near-optimal RL solutions for training tasks. This work relates to meta RL, contextual RL, and transfer learning, with a particular focus on zero-shot performance at test time, enabled by knowledge of the task parameters (also known as context). Our technical approach is based upon viewing each RL algorithm as a mapping from the MDP specifics to the near-optimal value function and policy and seek to approximate it with a hypernetwork that can generate near-optimal value functions and policies, given the parameters of the MDP. We show that, under certain conditions, this mapping can be considered as a supervised learning problem. We empirically evaluate the effectiveness of our method for zero-shot transfer to new reward and transition dynamics on a series of continuous control tasks from DeepMind Control Suite. Our method demonstrates significant improvements over baselines from multitask and meta RL approaches.
2023-06-26
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
arxiv.org