LuckyMera: a Modular AI Framework for Building Hybrid NetHack Agents
Luigi Quarantiello, Simone Marzeddu, Antonio Guzzi, Vincenzo Lomonaco

TL;DR
LuckyMera is a modular AI framework designed for developing and testing hybrid neural and symbolic agents in the complex roguelike game NetHack, facilitating rapid development and benchmarking of AI strategies.
Contribution
It introduces a flexible, extensible framework with pre-built modules and utilities for training and analyzing AI agents in NetHack, advancing research in game-based AI development.
Findings
Achieved state-of-the-art performance in NetHack
Validated the effectiveness of hybrid AI modules
Provided a comprehensive, open-source toolkit for AI research
Abstract
In the last few decades we have witnessed a significant development in Artificial Intelligence (AI) thanks to the availability of a variety of testbeds, mostly based on simulated environments and video games. Among those, roguelike games offer a very good trade-off in terms of complexity of the environment and computational costs, which makes them perfectly suited to test AI agents generalization capabilities. In this work, we present LuckyMera, a flexible, modular, extensible and configurable AI framework built around NetHack, a popular terminal-based, single-player roguelike video game. This library is aimed at simplifying and speeding up the development of AI agents capable of successfully playing the game and offering a high-level interface for designing game strategies. LuckyMera comes with a set of off-the-shelf symbolic and neural modules (called "skills"): these modules can be…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsReinforcement Learning in Robotics · Artificial Intelligence in Games · Data Stream Mining Techniques
MethodsLib
