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Keywords = Dark Souls III

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12 pages, 2519 KiB  
Article
Novel Reinforcement Learning Research Platform for Role-Playing Games
by Petra Csereoka, Bogdan-Ionuţ Roman, Mihai Victor Micea  and Călin-Adrian Popa
Mathematics 2022, 10(22), 4363; https://doi.org/10.3390/math10224363 - 20 Nov 2022
Cited by 5 | Viewed by 2874
Abstract
The latest achievements in the field of reinforcement learning have encouraged the development of vision-based learning methods that compete with human-provided results obtained on various games and training environments. Convolutional neural networks together with Q-learning-based approaches have managed to solve and outperform human [...] Read more.
The latest achievements in the field of reinforcement learning have encouraged the development of vision-based learning methods that compete with human-provided results obtained on various games and training environments. Convolutional neural networks together with Q-learning-based approaches have managed to solve and outperform human players in environments such as Atari 2600, Doom or StarCraft II, but the niche of 3D realistic games with a high degree of freedom of movement and rich graphics remains unexplored, despite having the highest resemblance to real-world situations. In this paper, we propose a novel testbed to push the limits of deep learning methods, namely an OpenAI Gym-like environment based on Dark Souls III, a notoriously difficult role-playing game, where even human players have reportedly struggled. We explore two types of architectures, Deep Q-Network and Deep Recurrent Q-Network, providing the results of a first incursion into this new problem class. The source code for the training environment and baselines is made available. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Based Methods and Applications)
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