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Katakomba: Tools and Benchmarks for Data-Driven NetHack

Vladislav Kurenkov · Alexander Nikulin · Denis Tarasov · Sergey Kolesnikov

Great Hall & Hall B1+B2 (level 1) #106
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Thu 14 Dec 3 p.m. PST — 5 p.m. PST


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: tool-wise, implementation-wise, and benchmark-wise. To address them, we develop an open-source library that provides workflow fundamentals familiar to the ORL community: pre-defined D4RL-style tasks, uncluttered baseline implementations, and reliable evaluation tools with accompanying configs and logs synced to the cloud.

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