MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research

Understand

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.