39 citations · 76 across the 8 of their papers we have counts for
8 papers
Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX
Clément Bonnet, Daniel Luo, Donal Byrne +21
Open-source reinforcement learning (RL) environments have played a crucial role in driving progress in the development of AI algorithms. In modern RL research, there is a need for…
Mava: a research library for distributed multi-agent reinforcement learning in JAX
Ruan de Kock, Omayma Mahjoub, Sasha Abramowitz +5
Multi-agent reinforcement learning (MARL) research is inherently computationally expensive and it is often difficult to obtain a sufficient number of experiment samples to test hyp…
Designing a Prospective COVID-19 Therapeutic with Reinforcement Learning
Marcin J. Skwark, Nicolás López Carranza, Thomas Pierrot +6
The SARS-CoV-2 pandemic has created a global race for a cure. One approach focuses on designing a novel variant of the human angiotensin-converting enzyme 2 (ACE2) that binds more…
Offline Reinforcement Learning Hands-On
Louis Monier, Jakub Kmec, Alexandre Laterre +4
Offline Reinforcement Learning (RL) aims to turn large datasets into powerful decision-making engines without any online interactions with the environment. This great promise has m…
A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learning
Arnu Pretorius, Scott Cameron, Elan van Biljon +6
Multi-agent reinforcement learning has recently shown great promise as an approach to networked system control. Arguably, one of the most difficult and important tasks for which la…
Learning Compositional Neural Programs for Continuous Control
Thomas Pierrot, Nicolas Perrin, Feryal Behbahani +4
We propose a novel solution to challenging sparse-reward, continuous control problems that require hierarchical planning at multiple levels of abstraction. Our solution, dubbed Alp…
Learning Compositional Neural Programs with Recursive Tree Search and Planning
Thomas Pierrot, Guillaume Ligner, Scott Reed +6
We propose a novel reinforcement learning algorithm, AlphaNPI, that incorporates the strengths of Neural Programmer-Interpreters (NPI) and AlphaZero. NPI contributes structural bia…
Ranked Reward: Enabling Self-Play Reinforcement Learning for Combinatorial Optimization
Alexandre Laterre, Yunguan Fu, Mohamed Khalil Jabri +6
Adversarial self-play in two-player games has delivered impressive results when used with reinforcement learning algorithms that combine deep neural networks and tree search. Algor…