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JRM Vol.36 No.3 pp. 580-588
doi: 10.20965/jrm.2024.p0580
(2024)

Paper:

Enhanced Naive Agent in Angry Birds AI Competition via Exploitation-Oriented Learning

Kazuteru Miyazaki ORCID Icon

National Institution for Academic Degrees and Quality Enhancement of Higher Education
1-29-1 Gakuennishimachi, Kodaira, Tokyo 185-8587, Japan

Received:
November 15, 2023
Accepted:
February 24, 2024
Published:
June 20, 2024
Keywords:
reinforcement learning, multi-agent learning, profit sharing, Q-learning, game
Abstract

The Angry Birds AI Competition engages artificial intelligence agents in a contest based on the game Angry Birds. This tournament has been conducted annually since 2012, with participants competing for high scores. The organizers of this competition provide a basic agent, termed “Naive Agent,” as a baseline indicator. This study enhanced the Naive Agent by integrating a profit-sharing approach known as exploitation-oriented learning, which is a type of experience-enhanced learning. The effectiveness of this method was substantiated through numerical experiments. Additionally, this study explored the use of level selection learning within a multi-agent environment and validated the utility of the rationality theorem concerning the indirect rewards in this environment.

Screenshot of the Angry Birds game

Screenshot of the Angry Birds game

Cite this article as:
K. Miyazaki, “Enhanced Naive Agent in Angry Birds AI Competition via Exploitation-Oriented Learning,” J. Robot. Mechatron., Vol.36 No.3, pp. 580-588, 2024.
Data files:
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Last updated on Jul. 12, 2024