Neuro-Fuzzy Actor Critic Reinforcement Learning for determination of optimal timing plans

Autor:	Montasir M Abbas, Linsen Chong
Rok vydání:	2010
Předmět:	Engineering Artificial neural network Neuro-fuzzy business.industry Intersection (set theory) Multi-agent system Fuzzy set Reinforcement learning Artificial intelligence business Flow network Intelligent transportation system
Zdroj:	ITSC
DOI:	10.1109/itsc.2010.5625260
Popis:	The purpose of timing plan optimization is to decrease delay and increase the overall performance of transportation network. This paper presents an agent-based reinforcement learning framework to train optimization agents to take appropriate actions according to perceived traffic states. Neuro-Fuzzy Actor-Critic Reinforcement Learning (NFACRL) method is applied in isolated intersection control. The control agent gets knowledge of traffic states after the learning process and determines the optimal phase durations required to minimize vehicle delay at a given intersection.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::b1b31fe37ce9dad7735988804889cbd2 https://doi.org/10.1109/itsc.2010.5625260 Zobrazit plný text záznamu