Crawling in Rogue's dungeons with (partitioned) A3C
Machine Learning
2019-12-03 v3 Machine Learning
Abstract
Rogue is a famous dungeon-crawling video-game of the 80ies, the ancestor of its gender. Rogue-like games are known for the necessity to explore partially observable and always different randomly-generated labyrinths, preventing any form of level replay. As such, they serve as a very natural and challenging task for reinforcement learning, requiring the acquisition of complex, non-reactive behaviors involving memory and planning. In this article we show how, exploiting a version of A3C partitioned on different situations, the agent is able to reach the stairs and descend to the next level in 98% of cases.
Cite
@article{arxiv.1804.08685,
title = {Crawling in Rogue's dungeons with (partitioned) A3C},
author = {Andrea Asperti and Daniele Cortesi and Francesco Sovrano},
journal= {arXiv preprint arXiv:1804.08685},
year = {2019}
}
Comments
Accepted at the Fourth International Conference on Machine Learning, Optimization, and Data Science (LOD 2018)