In deep reinforcement learning, a pruned network is a good network

no code implementations • 19 Feb 2024 • Johan Obando-Ceron, Aaron Courville, Pablo Samuel Castro

Recent work has shown that deep reinforcement learning agents have difficulty in effectively using their network parameters.

reinforcement-learning

Mixtures of Experts Unlock Parameter Scaling for Deep RL

no code implementations • 13 Feb 2024 • Johan Obando-Ceron, Ghada Sokar, Timon Willi, Clare Lyle, Jesse Farebrother, Jakob Foerster, Gintare Karolina Dziugaite, Doina Precup, Pablo Samuel Castro

The recent rapid progress in (self) supervised learning models is in large part predicted by empirical scaling laws: a model's performance scales proportionally to its size.

reinforcement-learning Self-Supervised Learning

Bigger, Better, Faster: Human-level Atari with human-level efficiency

3 code implementations • 30 May 2023 • Max Schwarzer, Johan Obando-Ceron, Aaron Courville, Marc Bellemare, Rishabh Agarwal, Pablo Samuel Castro

We introduce a value-based RL agent, which we call BBF, that achieves super-human performance in the Atari 100K benchmark.

Atari Games 100k

JaxPruner: A concise library for sparsity research

1 code implementation • 27 Apr 2023 • Joo Hyung Lee, Wonpyo Park, Nicole Mitchell, Jonathan Pilault, Johan Obando-Ceron, Han-Byul Kim, Namhoon Lee, Elias Frantar, Yun Long, Amir Yazdanbakhsh, Shivani Agrawal, Suvinay Subramanian, Xin Wang, Sheng-Chun Kao, Xingyao Zhang, Trevor Gale, Aart Bik, Woohyun Han, Milen Ferev, Zhonglin Han, Hong-Seok Kim, Yann Dauphin, Gintare Karolina Dziugaite, Pablo Samuel Castro, Utku Evci

This paper introduces JaxPruner, an open-source JAX-based pruning and sparse training library for machine learning research.