  Traffic signal control has a great impact on alleviating traffic congestion
in modern cities. Deep reinforcement learning (RL) has been widely used for
this task in recent years, demonstrating promising performance but also facing
many challenges such as limited performances and sample inefficiency. To handle
these challenges, MTLight is proposed to enhance the agent observation with a
latent state, which is learned from numerous traffic indicators. Meanwhile,
multiple auxiliary and supervisory tasks are constructed to learn the latent
state, and two types of embedding latent features, the task-specific feature
and task-shared feature, are used to make the latent state more abundant.
Extensive experiments conducted on CityFlow demonstrate that MTLight has
leading convergence speed and asymptotic performance. We further simulate under
peak-hour pattern in all scenarios with increasing control difficulty and the
results indicate that MTLight is highly adaptable.
