Crowd Simulation Via Multi-Agent Reinforcement Learning
Crowd Simulation Via Multi-Agent Reinforcement Learning
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DOI：
10.1609/aiide.v6i1.12390
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发表时间：
2010-10
期刊：
影响因子：
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通讯作者：
Lisa A. Torrey
中科院分区：
文献类型：
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作者：
Lisa A. Torrey
人工智能经常用于控制电影和游戏中的虚拟角色。当这些角色出现在人群中时，控制它们被称为人群模拟。在本文中，我建议人群模拟可以通过多智能体强化学习来完成，通过这种方法，一组智能体可以学习在其环境中自主行动。我提出了一个案例研究，探讨了这种方法的挑战和好处，并鼓励在娱乐媒体中开发人工智能的学习技术。
Artificial intelligence is frequently used to control virtual characters in movies and games. When these characters appear in crowds, controlling them is called crowd simulation. In this paper, I suggest that crowd simulation could be accomplished by multi-agent reinforcement learning, a method by which groups of agents can learn to act autonomously in their environment. I present a case study that explores the challenges and benefits of this type of approach and encourages the development of learning techniques for AI in entertainment media.