Intelligent Coordination among Multiple Traffic Intersections Using Multi-Agent Reinforcement Learning
This addresses traffic management for urban planners, but it is incremental as it builds on existing A3C methods.
The paper tackled traffic congestion at multiple intersections by implementing multi-agent reinforcement learning with A3C, observing reductions in congestion through methods like asynchronous control, competitive play, and cooperative global rewards.
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.