  The steady increase in the number of vehicles operating on the highways
continues to exacerbate congestion, accidents, energy consumption, and
greenhouse gas emissions. Emerging mobility systems, e.g., connected and
automated vehicles (CAVs), have the potential to directly address these issues
and improve transportation network efficiency and safety. In this paper, we
consider a highway merging scenario and propose a framework for coordinating
CAVs such that stop-and-go driving is eliminated. We use a decentralized form
of the actor-critic approach to deep reinforcement learning$-$multi-agent deep
deterministic policy gradient. We demonstrate the coordination of CAVs through
numerical simulations and show that a smooth traffic flow is achieved by
eliminating stop-and-go driving. Videos and plots of the simulation results can
be found at this supplemental
$\href{https://sites.google.com/view/ud-ids-lab/MADRL}{\text{site}}$.
