  Progress in deep reinforcement learning (RL) is heavily driven by the
availability of challenging benchmarks used for training agents. However,
benchmarks that are widely adopted by the community are not explicitly designed
for evaluating specific capabilities of RL methods. While there exist
environments for assessing particular open problems in RL (such as exploration,
transfer learning, unsupervised environment design, or even language-assisted
RL), it is generally difficult to extend these to richer, more complex
environments once research goes beyond proof-of-concept results. We present
MiniHack, a powerful sandbox framework for easily designing novel RL
environments. MiniHack is a one-stop shop for RL experiments with environments
ranging from small rooms to complex, procedurally generated worlds. By
leveraging the full set of entities and environment dynamics from NetHack, one
of the richest grid-based video games, MiniHack allows designing custom RL
testbeds that are fast and convenient to use. With this sandbox framework,
novel environments can be designed easily, either using a human-readable
description language or a simple Python interface. In addition to a variety of
RL tasks and baselines, MiniHack can wrap existing RL benchmarks and provide
ways to seamlessly add additional complexity.
