  Unsupervised environment design (UED) is a form of automatic curriculum
learning for training robust decision-making agents to zero-shot transfer into
unseen environments. Such autocurricula have received much interest from the RL
community. However, UED experiments, based on CPU rollouts and GPU model
updates, have often required several weeks of training. This compute
requirement is a major obstacle to rapid innovation for the field. This work
introduces the minimax library for UED training on accelerated hardware. Using
JAX to implement fully-tensorized environments and autocurriculum algorithms,
minimax allows the entire training loop to be compiled for hardware
acceleration. To provide a petri dish for rapid experimentation, minimax
includes a tensorized grid-world based on MiniGrid, in addition to reusable
abstractions for conducting autocurricula in procedurally-generated
environments. With these components, minimax provides strong UED baselines,
including new parallelized variants, which achieve over 120$\times$ speedups in
wall time compared to previous implementations when training with equal batch
sizes. The minimax library is available under the Apache 2.0 license at
https://github.com/facebookresearch/minimax.
