Learning a Robust Multiagent Driving Policy for Traffic Congestion Reduction

The advent of automated and autonomous vehicles (AVs) creates opportunities to achieve system-level goals using multiple AVs, such as traffic congestion reduction. Past research has shown that multiagent congestion-reducing driving policies can be learned in a variety of simulated scenarios. While initial proofs of concept were in small, closed traffic networks with a centralized controller, recently successful results have been demonstrated in more realistic settings with distributed control policies operating in open road networks where vehicles enter and leave. However, these driving policies were mostly tested under the same conditions they were trained on, and have not been thoroughly tested for robustness to different traffic conditions, which is a critical requirement in real-world scenarios. This paper presents a learned multiagent driving policy that is robust to a variety of open-network traffic conditions, including vehicle flows, the fraction of AVs in traffic, AV placement, and different merging road geometries. A thorough empirical analysis investigates the sensitivity of such a policy to the amount of AVs in both a simple merge network and a more complex road with two merging ramps. It shows that the learned policy achieves significant improvement over simulated human-driven policies even with AV penetration as low as 2%. The same policy is also shown to be capable of reducing traffic congestion in more complex roads with two merging ramps.