no code implementations • 6 Oct 2022 • Jad Rahme
In Chapter 2, inspired by statistical physics, we develop a novel approach to Reinforcement Learning (RL) that not only learns optimal policies with enhanced desirable properties but also sheds new light on maximum entropy RL.
no code implementations • NeurIPS 2021 • Dibya Ghosh, Jad Rahme, Aviral Kumar, Amy Zhang, Ryan P. Adams, Sergey Levine
Generalization is a central challenge for the deployment of reinforcement learning (RL) systems in the real world.
no code implementations • ICLR 2021 • Jad Rahme, Samy Jelassi, S. Matthew Weinberg
This not only circumvents the need for an expensive hyper-parameter search (as in prior work), but also provides a principled metric to compare the performance of two auctions (absent from prior work).
1 code implementation • 2 Mar 2020 • Jad Rahme, Samy Jelassi, Joan Bruna, S. Matthew Weinberg
Designing an incentive compatible auction that maximizes expected revenue is a central problem in Auction Design.
no code implementations • 24 Jun 2019 • Jad Rahme, Ryan P. Adams
The central object in the statistical physics abstraction is the idea of a partition function $\mathcal{Z}$, and here we construct a partition function from the ensemble of possible trajectories that an agent might take in a Markov decision process.