Reinforcement Learning Based for Traffic Signal Monitoring and Management

Reinforcement Learning Based for Traffic Signal Monitoring and Management
Authors : Ms Namrata S. Jadhao, Mr.Parag A. Kulkarni
Publication Date: 30-06-2012


Author(s):  Ms Namrata S. Jadhao, Mr.Parag A. Kulkarni

Published in:   International Journal of Engineering Research & Technology

License:  This work is licensed under a Creative Commons Attribution 4.0 International License.

Website: www.ijert.org

Volume/Issue:   Vol.1 - Issue 4 (June- 2012)

e-ISSN:   2278-0181


ABSTRACT - To obtain more accurate patterns insight into traffic signal by analyzing within and between day variations in traffic volumes, using the methods of machine learning. Proposed system is based on reinforcement learning (RL) for traffic signal control. RL uses multi agent structure where vehicles and traffic signals are working as agents. Reinforcement learning is to learn the optimal policy by a trial-and-error process including observing the environment and choosing an action according to current states and receiving rewards from the environment. The policy which maximizes the expected long-term reward is considered as the optimal one System objective is to optimize traffic states using RL-algorithm. This paper describes traffic management using reinforcement learning based on paramic simulation. Expected outcomes of the algorithm will work more efficiently than other traffic system.


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