Show simple item record

dc.contributor.authorZhang, Zhi
dc.date.accessioned2020-05-20T16:57:58Z
dc.date.available2020-05-20T16:57:58Z
dc.date.created2019-05
dc.date.issued2019-04-30
dc.date.submittedMay 2019
dc.identifier.urihttp://hdl.handle.net/1853/62705
dc.description.abstractTraffic congestion in metropolitan areas is a world-wide problem that can be ameliorated by traffic lights that respond dynamically to real-time conditions. Recent studies applying deep reinforcement learning (RL) to optimize single traffic lights have shown significant improvement over conventional control. However, optimization of global traffic condition over a large road network fundamentally is a cooperative multi-agent control problem, for which single-agent RL is not suitable due to environment non-stationarity and infeasibility of optimizing over an exponential joint-action space. Motivated by these challenges, we propose QCOMBO, a simple yet effective multi-agent reinforcement learning (MARL) algorithm that combines the advantages of independent and centralized learning. We ensure scalability by selecting actions from individually optimized utility functions, which are shaped to maximize global performance via a novel consistency regularization loss between individual utility and a global action-value function. Experiments on diverse road topologies and traffic flow conditions in the SUMO traffic simulator show competitive performance of QCOMBO versus recent state-of-the-art MARL algorithms. We further show that policies trained on small sub-networks can effectively generalize to larger networks under different traffic flow conditions, providing empirical evidence for the suitability of MARL for intelligent traffic control.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectTraffic light control
dc.subjectMulti-agent reinforcement learning
dc.subjectDeep reinforcement learning
dc.titleIntegrating independent and centralized multi-agent reinforcement learning for traffic signal network optimization
dc.typeThesis
dc.description.degreeM.S.
dc.contributor.departmentComputer Science
thesis.degree.levelMasters
dc.contributor.committeeMemberZha, Hongyuan
dc.contributor.committeeMemberIsaac, Tobin
dc.contributor.committeeMemberYe, Xiaojing
dc.date.updated2020-05-20T16:57:58Z


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record