Approaches That Use Domain-Specific Expertise: Behavioral-Cloning-Based Advantage Actor-Critic in Basketball Games

Taehyeok Choi, Kyungeun Cho () and Yunsick Sung ()
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Taehyeok Choi: Department of Multimedia Engineering, Dongguk University-Seoul, 30, Pildongro-1-gil, Jung-gu, Seoul 04620, Republic of Korea
Kyungeun Cho: Department of Multimedia Engineering, Dongguk University-Seoul, 30, Pildongro-1-gil, Jung-gu, Seoul 04620, Republic of Korea
Yunsick Sung: Department of Multimedia Engineering, Dongguk University-Seoul, 30, Pildongro-1-gil, Jung-gu, Seoul 04620, Republic of Korea

Mathematics, 2023, vol. 11, issue 5, 1-14

Abstract: Research on the application of artificial intelligence (AI) in games has recently gained momentum. Most commercial games still use AI based on a finite state machine (FSM) due to complexity and cost considerations. However, FSM-based AI decreases user satisfaction given that it performs the same patterns of consecutive actions in the same situations. This necessitates a new AI approach that applies domain-specific expertise to existing reinforcement learning algorithms. We propose a behavioral-cloning-based advantage actor-critic (A2C) that improves learning performance by applying a behavioral cloning algorithm to an A2C algorithm in basketball games. The state normalization, reward function, and episode classification approaches are used with the behavioral-cloning-based A2C. The results of the comparative experiments with the traditional A2C algorithms validated the proposed method. Our proposed method using existing approaches solved the difficulty of learning in basketball games.

Keywords: game AI; reinforcement learning; imitation learning; basketball game (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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