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In the last few decades we have witnessed a significant development inArtificial Intelligence (AI) thanks to the availability of a variety oftestbeds, mostly based on simulated environments and video games. Among those,roguelike games offer a very good trade-off in terms of complexity of theenvironment and computational costs, which makes them perfectly suited to testAI agents generalization capabilities. In this work, we present LuckyMera, aflexible, modular, extensible and configurable AI framework built aroundNetHack, a popular terminal-based, single-player roguelike video game. Thislibrary is aimed at simplifying and speeding up the development of AI agentscapable of successfully playing the game and offering a high-level interfacefor designing game strategies. LuckyMera comes with a set of off-the-shelfsymbolic and neural modules (called "skills"): these modules can be eitherhard-coded behaviors, or neural Reinforcement Learning approaches, with thepossibility of creating compositional hybrid solutions. Additionally, LuckyMeracomes with a set of utility features to save its experiences in the form oftrajectories for further analysis and to use them as datasets to train neuralmodules, with a direct interface to the NetHack Learning Environment andMiniHack. Through an empirical evaluation we validate our skills implementationand propose a strong baseline agent that can reach state-of-the-artperformances in the complete NetHack game. LuckyMera is open-source andavailable at https://github.com/Pervasive-AI-Lab/LuckyMera.

LuckyMera: a Modular AI Framework for Building Hybrid NetHack Agents