Quantum Reinforcement Learning Applied to Board Games

• Type: Article in International Conference Proceedings Book
• Date: 2021

Authors

Teixeira, M (Author) Other; Ana Paula Rocha (Author) FEUP; António J. M. Castro (Author) Other

Conference proceedings International

Title: WI-IAT '21: IEEE/WIC/ACM International Conference on Web Intelligence, Melbourne VIC Australia, December 14 - 17, 2021

Pages: 343-350

2021 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2021

14 December 2021 through 17 December 2021

Indexing

Scopus - 343 Citations

Other information

Authenticus ID: P-00W-G9K

DOI: 10.1145/3486622.3493944

Abstract (EN): Reinforcement learning is a machine learning paradigm where an agent learns how to optimize its behavior solely through its interaction with the environment. It has been extensively studied and successfully applied to complex problems of many different domains in the past decades, i.e., robotics, games, scheduling. However, the performance of these algorithms becomes limited as the complexity and dimension of the state-action space increases. Recent advances in quantum computing and quantum information have sparked interest in possible applications to machine learning. By taking advantage of quantum mechanics, it is possible to efficiently process immense quantities of information and improve computational speed. In this work, we combined quantum computing with reinforcement learning and studied its application to a board game to assess the benefits that it can introduce, namely its impact on the learning efficiency of an agent. From the results, we concluded that the proposed quantum exploration policy improved the convergence rate of the agent and promoted a more efficient exploration of the state space. © 2021 ACM.

Language: English

Type (Professor's evaluation): Scientific