  We propose a model-free reinforcement learning method for controlling mixed
autonomy traffic in simulated traffic networks with through-traffic-only
two-way and four-way intersections. Our method utilizes multi-agent policy
decomposition which allows decentralized control based on local observations
for an arbitrary number of controlled vehicles. We demonstrate that, even
without reward shaping, reinforcement learning learns to coordinate the
vehicles to exhibit traffic signal-like behaviors, achieving near-optimal
throughput with 33-50% controlled vehicles. With the help of multi-task
learning and transfer learning, we show that this behavior generalizes across
inflow rates and size of the traffic network. Our code, models, and videos of
results are available at
https://github.com/ZhongxiaYan/mixed_autonomy_intersections.
