# Crawling in Rogue's dungeons with (partitioned) A3C

arXiv: https://arxiv.org/abs/1804.08685 · HTML: https://mlsanity.com/paper/1804.08685/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2018.

## Built on by (most cited first, 1 of 1)

- [MicroRacer: a didactic environment for Deep Reinforcement Learning](https://mlsanity.com/paper/2203.10494/) (2022)

## Abstract (arXiv)

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.