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Crowd behavioural simulation via multi-agent reinforcement learning

A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of requirements for the degree of Master of Science. Johannesburg, 2015. / 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.

Identiferoai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:wits/oai:wiredspace.wits.ac.za:10539/19323
Date January 2016
CreatorsLim, Sheng Yan
Source SetsSouth African National ETD Portal
LanguageEnglish
Detected LanguageEnglish
TypeThesis
Formatapplication/pdf

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