Crowd Simulation Via Multi-Agent Reinforcement Learning

Abstract

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.