XLand-MiniGrid: Scalable Meta-Reinforcement Learning Environments in JAX
This provides a resource-efficient platform for meta-RL researchers, though it is incremental as it builds on existing environment concepts.
The authors tackled the need for scalable and accessible meta-reinforcement learning environments by introducing XLand-MiniGrid, a suite of grid-world tools in JAX that achieves millions of steps per second during training and includes millions of unique tasks.
Inspired by the diversity and depth of XLand and the simplicity and minimalism of MiniGrid, we present XLand-MiniGrid, a suite of tools and grid-world environments for meta-reinforcement learning research. Written in JAX, XLand-MiniGrid is designed to be highly scalable and can potentially run on GPU or TPU accelerators, democratizing large-scale experimentation with limited resources. Along with the environments, XLand-MiniGrid provides pre-sampled benchmarks with millions of unique tasks of varying difficulty and easy-to-use baselines that allow users to quickly start training adaptive agents. In addition, we have conducted a preliminary analysis of scaling and generalization, showing that our baselines are capable of reaching millions of steps per second during training and validating that the proposed benchmarks are challenging. XLand-MiniGrid is open-source and available at https://github.com/dunnolab/xland-minigrid.