Training Game AI Using Reinforcement Learning and Deep Reinforcement Learning: Methods and Approaches
Keywords:
Game AI training, Intensive learning, Deep reinforcement learningAbstract
This article explores the importance of game AI training as a significant interdisciplinary domain at the intersection of computer science and artificial intelligence, with particular emphasis on its central role in reinforcement learning research. Game AI training not only represents a prominent topic in technical practice but also serves as a critical testbed for driving innovation in artificial intelligence theory and methodology. Currently, the practical implementation of game AI training faces numerous challenges, including the dual pressures of ethical considerations and technological innovation. These challenges necessitate in-depth investigation of key technical issues in game AI training, such as precise analysis of coefficients and delayed feedback, effective exploration of high-dimensional state spaces and complex action spaces, and improvement of policy learning robustness in unstable environments. In response to these challenges, this paper proposes an innovative solution grounded in recent advances in deep reinforcement learning. By integrating the complementary strengths of reinforcement learning and deep learning, we construct a foundational deep reinforcement learning framework incorporating an attention mechanism. This framework is designed to address collective intelligence problems in complex environments by intelligently allocating attention resources, thereby enhancing the decision-making efficiency and accuracy of AI systems when processing massive information streams and operating within dynamic environments. This study not only offers a novel technical pathway for game AI training but also provides theoretical support and practical guidance for the broader application of artificial intelligence across diverse domains.
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