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.

翻译：NetHack被誉为强化学习研究的前沿领域，在该领域中基于学习的方法仍需追赶基于规则的解决方案。实现突破的方向之一，是借鉴近期机器人学、推荐系统等领域的发展，在离线强化学习框架下使用预采集数据集。近期虽已发布大规模NetHack数据集，此举虽属必要进展，但尚未在离线强化学习社区获得广泛采用。本研究认为，采用障碍主要存在于工具、实现与基准三个层面。为此，我们开发了一套开源库，提供离线强化学习社区所熟悉的基础工作流：预定义的D4RL风格任务、简洁的基线实现、可靠的评估工具，并附有同步至云端的配置文件与日志。