  Multi-agent Deep Reinforcement Learning (MADRL) based traffic signal control
becomes a popular research topic in recent years. To alleviate the scalability
issue of completely centralized RL techniques and the non-stationarity issue of
completely decentralized RL techniques on large-scale traffic networks, some
literature utilizes a regional control approach where the whole network is
firstly partitioned into multiple disjoint regions, followed by applying the
centralized RL approach to each region. However, the existing partitioning
rules either have no constraints on the topology of regions or require the same
topology for all regions. Meanwhile, no existing regional control approach
explores the performance of optimal joint action in an exponentially growing
regional action space when intersections are controlled by 4-phase traffic
signals (EW, EWL, NS, NSL). In this paper, we propose a novel RL training
framework named RegionLight to tackle the above limitations. Specifically, the
topology of regions is firstly constrained to a star network which comprises
one center and an arbitrary number of leaves. Next, the network partitioning
problem is modeled as an optimization problem to minimize the number of
regions. Then, an Adaptive Branching Dueling Q-Network (ABDQ) model is proposed
to decompose the regional control task into several joint signal control
sub-tasks corresponding to particular intersections. Subsequently, these
sub-tasks maximize the regional benefits cooperatively. Finally, the global
control strategy for the whole network is obtained by concatenating the optimal
joint actions of all regions. Experimental results demonstrate the superiority
of our proposed framework over all baselines under both real and synthetic
datasets in all evaluation metrics.
