  Traffic signal control (TSC) is a complex and important task that affects the
daily lives of millions of people. Reinforcement Learning (RL) has shown
promising results in optimizing traffic signal control, but current RL-based
TSC methods are mainly trained in simulation and suffer from the performance
gap between simulation and the real world. In this paper, we propose a
simulation-to-real-world (sim-to-real) transfer approach called UGAT, which
transfers a learned policy trained from a simulated environment to a real-world
environment by dynamically transforming actions in the simulation with
uncertainty to mitigate the domain gap of transition dynamics. We evaluate our
method on a simulated traffic environment and show that it significantly
improves the performance of the transferred RL policy in the real world.

Machine Learning;Artificial Intelligence