Optimizing Traffic Lights with Multi-agent Deep Reinforcement Learning and V2X communication

• URL: http://arxiv.org/abs/2002.09853v1
• Date: Sun, 23 Feb 2020 07:43:12 GMT
• Title: Optimizing Traffic Lights with Multi-agent Deep Reinforcement Learning and V2X communication
• Authors: Azhar Hussain, Tong Wang and Cao Jiahua
• Abstract summary: We consider a system to optimize duration of traffic signals using multi-agent deep reinforcement learning and Vehicle-to-Everything (V2X) communication. This system aims at analyzing independent and shared rewards for multi-agents to control duration of traffic lights.
• Score: 5.40232936994133
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We consider a system to optimize duration of traffic signals using multi-agent deep reinforcement learning and Vehicle-to-Everything (V2X) communication. This system aims at analyzing independent and shared rewards for multi-agents to control duration of traffic lights. A learning agent traffic light gets information along its lanes within a circular V2X coverage. The duration cycles of traffic light are modeled as Markov decision Processes. We investigate four variations of reward functions. The first two are unshared-rewards: based on waiting number, and waiting time of vehicles between two cycles of traffic light. The third and fourth functions are: shared-rewards based on waiting cars, and waiting time for all agents. Each agent has a memory for optimization through target network and prioritized experience replay. We evaluate multi-agents through the Simulation of Urban MObility (SUMO) simulator. The results prove effectiveness of the proposed system to optimize traffic signals and reduce average waiting cars to 41.5 % as compared to the traditional periodic traffic control system.

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