Crowd behavioural simulation via multi-agent reinforcement learning

Abstract

Crowd simulation can be thought of as a group of entities interacting with one another. Traditionally, an animated entity would require precise scripts so that it can function in a virtual environment autonomously. Previous studies on crowd simulation have been used in real world applications but these methods are not learning agents and are therefore unable to adapt and change their behaviours. The state of the art crowd simulation methods include flow based, particle and strategy based models. A reinforcement learning agent could learn how to navigate, behave and interact in an environment without explicit design. Then a group of reinforcement learning agents should be able to act in a way that simulates a crowd. This thesis investigates the believability of crowd behavioural simulation via three multi-agent reinforcement learning methods. The methods are Q-learning in multi-agent markov decision processes model, joint state action Q-learning and joint state value iteration algorithm. The three learning methods are able to produce believable and realistic crowd behaviours.