WSEAS Transactions on Systems and Control

Print ISSN: 1991-8763
E-ISSN: 2224-2856

Volume 12, 2017

Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of WSEAS Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.

Volume 12, 2017

A Modular Deep-learning Environment for Rogue

AUTHORS: Andrea Asperti, Carlo De Pieri, Mattia Maldini, Gianmaria Pedrini, Francesco Sovrano

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ABSTRACT: Rogue is a famous dungeon-crawling video-game of the 80ies, the ancestor of its gender. Due to their nature, and in particular to the necessity to explore partially observable and always different labyrinths (no level replay), roguelike games are a very natural and challenging task for reinforcement learning and Q-learning, requiring the acquisition of complex, non-reactive behaviours involving memory and planning. In this article we present Rogueinabox: an environment allowing a simple interaction with the Rogue game, especially designed for the definition of automatic agents and their training via deep-learning techniques. We also show a few initial examples of agents, discuss their architecture and illustrate their behaviour.

KEYWORDS: Machine Learning, Deep Learning, Reinforcement Learning, QLearning, Hierarchical Reinforcement Learning, Planning, Imagination augmentation, Neural Network, Artificial Intelligence, Rogue, Labyrinth, Dungeon, Game, Situations, Asynchronous Actor-Critic Agents, Auxiliary tasks, Intrinsic reward, Sparse reward


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WSEAS Transactions on Systems and Control, ISSN / E-ISSN: 1991-8763 / 2224-2856, Volume 12, 2017, Art. #39, pp. 362-373

Copyright © 2017 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0

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