Intelligent Coordination among Multiple Traffic Intersections Using Multi-Agent Reinforcement Learning
Year: 2019
Type: preprint
Abstract: We use Asynchronous Advantage Actor Critic (A3C) for implementing an AI agent in the controllers that optimize flow of traffic across a single intersection and then extend it to multiple intersections by considering a multi-agent setting. We explore three different methodologies to address the multi-agent problem - (1) use of asynchronous property of A3C to control multiple intersections using a single agent (2) utilise self/competitive play among independent agents across multiple intersections and (3) ingest a global reward function among agents to introduce cooperative behavior between intersections. We observe that (1) & (2) leads to a reduction in traffic congestion. Additionally the use of (3) with (1) & (2) led to a further reduction in congestion.
Source: arXiv (Cornell University)
Authors: Tewari, Ujwal Padam, Bidawatka, Vishal, Raveendran, Varsha, Sudhakaran, Vinay, Shreeshail, Shreedhar Kodate, Kulkarni, Jayanth Prakash
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2994368799