Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX
Authors: Clément Bonnet,Daniel Luo,Donal John Byrne,Shikha Surana,Sasha Abramowitz,Paul Duckworth,Vincent Coyette,Laurence Illing Midgley,Elshadai Tegegn,Tristan Kalloniatis,Omayma Mahjoub,Matthew Macfarlane,Andries Petrus Smit,Nathan Grinsztajn,Raphael Boige,Cemlyn Neil Waters,Mohamed Ali Ali Mimouni,Ulrich Armel Mbou Sob,Ruan John de Kock,Siddarth Singh,Daniel Furelos-Blanco,Victor Le,Arnu Pretorius,Alexandre Laterre
Presentation: ICLR 2024,Poster

Abstract: 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 simulated environments that are performant, scalable, and modular to enable their utilization in a wider range of potential real-world applications.
Therefore, we present Jumanji, a suite of diverse RL environments specifically designed to be fast, flexible, and scalable.
Jumanji provides a suite of environments focusing on combinatorial problems frequently encountered in industry, as well as challenging general decision-making tasks.
By leveraging the efficiency of JAX and hardware accelerators like GPUs and TPUs, Jumanji enables rapid iteration of research ideas and large-scale experimentation, ultimately empowering more capable agents.
Unlike existing RL environment suites, Jumanji is highly customizable, allowing users to tailor the initial state distribution and problem complexity to their needs.
Furthermore, we provide actor-critic baselines for each environment, accompanied by preliminary findings on scaling and generalization scenarios.
Jumanji aims to set a new standard for speed, adaptability, and scalability of RL environments.