Agent Simulation

Agent simulation involves creating computational models of interacting entities (agents) to study complex systems. Current research focuses on improving simulation realism, particularly through advanced machine learning techniques like transformers and generative adversarial networks, and incorporating real-world data to reduce the "reality gap." These advancements are driving progress in diverse fields, including autonomous driving, business process optimization, and large language model evaluation, by enabling more accurate and efficient testing and analysis of complex scenarios.