Reinforcement Learning and Deep Reinforcement Learning for Game AI Training: Methods and Applications
Keywords:
Game AI training, Intensive learning, Deep reinforcement learningAbstract
Game AI training represents an interdisciplinary integration of computer science and artificial intelligence, serving as a primary testbed environment within the field of reinforcement learning. At the current stage, game AI training environments present challenges related to both ethical considerations and technical innovation. These challenges primarily center on feedback analysis of coefficients and delays, the high-dimensional state-action space environment, and the characteristics of unstable environments. Building upon recent advances in deep reinforcement learning, we propose a foundational deep reinforcement learning framework incorporating an attention mechanism. This framework is designed to address the problem of collective intelligence in complex environments.
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Copyright (c) 2026 Chenyu Shen
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