  Traffic signal control aims to coordinate traffic signals across
intersections to improve the traffic efficiency of a district or a city. Deep
reinforcement learning (RL) has been applied to traffic signal control recently
and demonstrated promising performance where each traffic signal is regarded as
an agent. However, there are still several challenges that may limit its
large-scale application in the real world. To make the policy learned from a
training scenario generalizable to new unseen scenarios, a novel Meta
Variationally Intrinsic Motivated (MetaVIM) RL method is proposed to learn the
decentralized policy for each intersection that considers neighbor information
in a latent way. Specifically, we formulate the policy learning as a
meta-learning problem over a set of related tasks, where each task corresponds
to traffic signal control at an intersection whose neighbors are regarded as
the unobserved part of the state. Then, a learned latent variable is introduced
to represent the task's specific information and is further brought into the
policy for learning. In addition, to make the policy learning stable, a novel
intrinsic reward is designed to encourage each agent's received rewards and
observation transition to be predictable only conditioned on its own history.
Extensive experiments conducted on CityFlow demonstrate that the proposed
method substantially outperforms existing approaches and shows superior
generalizability.
