Artificial Intelligence Gamers Based on Deep Reinforcement Learning

Haolin Wang

doi:10.54097/p9tv1494

Authors

Haolin Wang

DOI:

https://doi.org/10.54097/p9tv1494

Keywords:

Deep Leaning, AI Gamers, Machine learning.

Abstract

This study investigates the design and implementation of Artificial Intelligence (AI) game players based on deep reinforcement learning, offering a novel approach to autonomous decision-making and strategy acquisition in intelligent games. Initially, the fundamental principles and algorithms of deep reinforcement learning are introduced, along with the fusion of deep learning and reinforcement learning. Subsequently, existing research is reviewed, and the pros and cons of current methodologies are examined, highlighting the underlying issues and challenges. The utilization of AI players in mainstream games is then introduced, and the influence of AI players on contemporary games is analyzed. Through this analysis of AI players in mainstream games, the strengths and weaknesses of current AI players are identified, and recommendations for optimizing them are provided. This study holds significant implications for guiding the design and development of intelligent game players, while also enriching the application of deep reinforcement learning within the gaming domain.

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