Jaxnasium: Embracing PyTrees For Any-Agent, Cross-Suite Reinforcement Learning

Koen Ponse, Aske Plaat, Thomas M. Moerland

Hardware-accelerated reinforcement learning in JAX has gained rapid popularity in recent years and has proven to be a successful method of scaling online reinforcement learning. The resulting ecosystem, however, is fragmented: environment suites define their own interfaces, and algorithm implementations are typically written for a single suite, a single type of space, and either JAX-native or host-based environments. Here we present Jaxnasium, a library for JAX-based reinforcement learning, aiming to make the field more accessible. Jaxnasium represents observation and action spaces as PyTrees, and uses this structure to build networks and support multi-agent processing. Through this, its near-single-file implementations run on discrete, continuous, image, and composite spaces, as well as in multi-agent environments, without any changes to the algorithm code. The same files serve as readable, research-friendly algorithms, as well as importable algorithms for more applied workloads. Through a minimal Gymnasium-style interface and wrappers for 11 suites, over 300 environments are available on which a small PPO implementation with default hyperparameters is able to run. Additionally,…