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NetHack is known as the frontier of reinforcement learning research wherelearning-based methods still need to catch up to rule-based solutions. One ofthe promising directions for a breakthrough is using pre-collected datasetssimilar to recent developments in robotics, recommender systems, and more underthe umbrella of offline reinforcement learning (ORL). Recently, a large-scaleNetHack dataset was released; while it was a necessary step forward, it has yetto gain wide adoption in the ORL community. In this work, we argue that thereare three major obstacles for adoption: resource-wise, implementation-wise, andbenchmark-wise. To address them, we develop an open-source library thatprovides workflow fundamentals familiar to the ORL community: pre-definedD4RL-style tasks, uncluttered baseline implementations, and reliable evaluationtools with accompanying configs and logs synced to the cloud.

Katakomba: Tools and Benchmarks for Data-Driven NetHack