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The steady increase in the number of vehicles operating on the highwayscontinues to exacerbate congestion, accidents, energy consumption, andgreenhouse gas emissions. Emerging mobility systems, e.g., connected andautomated vehicles (CAVs), have the potential to directly address these issuesand improve transportation network efficiency and safety. In this paper, weconsider a highway merging scenario and propose a framework for coordinatingCAVs such that stop-and-go driving is eliminated. We use a decentralized formof the actor-critic approach to deep reinforcement learning$-$multi-agent deepdeterministic policy gradient. We demonstrate the coordination of CAVs throughnumerical simulations and show that a smooth traffic flow is achieved byeliminating stop-and-go driving. Videos and plots of the simulation results canbe found at this supplemental$\href{https://sites.google.com/view/ud-ids-lab/MADRL}{\text{site}}$.

A Multi-Agent Deep Reinforcement Learning Coordination Framework for Connected and Automated Vehicles at Merging Roadways