Katakomba: Tools and Benchmarks for Data-Driven NetHack

Understand

NetHack is known as the frontier of reinforcement learning research where learning-based methods still need to catch up to rule-based solutions.

• One of the promising directions for a breakthrough is using pre-collected datasets similar to recent developments in robotics, recommender systems, and more under the umbrella of offline reinforcement learning (ORL).
• Recently, a large-scale NetHack dataset was released; while it was a necessary step forward, it has yet to gain wide adoption in the ORL community.
• In this work, we argue that there are three major obstacles for adoption: resource-wise, implementation-wise, and benchmark-wise.