  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.
