Conference Publication Details
Mandatory Fields
Mannion, P,Duggan, J,Howley, E,Shakshuki, E
6TH INTERNATIONAL CONFERENCE ON AMBIENT SYSTEMS, NETWORKS AND TECHNOLOGIES (ANT-2015), THE 5TH INTERNATIONAL CONFERENCE ON SUSTAINABLE ENERGY INFORMATION TECHNOLOGY (SEIT-2015)
Parallel Reinforcement Learning for Traffic Signal Control
2015
January
Published
1
WOS: 7 ()
Optional Fields
Reinforcement Learning Parallel Learning Multi Agent Systems Intelligent Transportation Systems Adaptive Traffic Signal Control Smart Cities
956
961
Developing Adaptive Traffic Signal Control strategies for efficient urban traffic management is a challenging problem, which is not easily solved. Reinforcement Learning (RL) has been shown to be a promising approach when applied to traffic signal control (TSC) problems. When using RL agents for TSC, difficulties may arise with respect to convergence times and performance. This is especially pronounced on complex intersections with many different phases, due to the increased size of the state action space. Parallel Learning is an emerging technique in RL literature, which allows several learning agents to pool their experiences while learning concurrently on the same problem. Here we present an extension to a leading published work on RL for TSC, which leverages the benefits of Parallel Learning to increase exploration and reduce delay times and queue lengths. (C) 2015 The Authors. Published by Elsevier B.V.
10.1016/j.procs.2015.05.172
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