MiniHack is a sandbox framework for easily designing rich and diverse environments for Reinforcement Learning (RL). Based on the game of NetHack, MiniHack uses the NetHack Learning Environment (NLE) to communicate with the game and to provide a convenient interface for customly created RL training and test environments of varying complexity. Check out our NeurIPS 2020 paper and recent blogpost.
MiniHack comes with a large list of challenging tasks. However, it is primarily built for easily designing new ones. The motivation behind MiniHack is to be able to perform RL experiments in a controlled setting while being able to increasingly scale the complexity of the tasks. MiniHack already comes with a large list of challenging tasks.
To do this, MiniHack leverages the so-called description files written using a human-readable probabilistic-programming-like domain-specific language. With just a few lines of code, people can generate a large variety of Gym environments, controlling every little detail, from the location and types of monsters, to the traps, objects, and terrain of the level, all while introducing randomness that challenges generalization capabilities of RL agents.