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

Abstract

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.