Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX

arXiv:2306.09884v2 Announce Type: replace
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 …

abstract ai algorithms algorithms arxiv cs.ai cs.lg development diverse driving environments jax modern modular progress reinforcement reinforcement learning research role scalable type