  The prevailing reinforcement-learning-based traffic signal control methods
are typically staging-optimizable or duration-optimizable, depending on the
action spaces. In this paper, we propose a novel control architecture, TBO,
which is based on hybrid proximal policy optimization. To the best of our
knowledge, TBO is the first RL-based algorithm to implement synchronous
optimization of the staging and duration. Compared to discrete and continuous
action spaces, hybrid action space is a merged search space, in which TBO
better implements the trade-off between frequent switching and unsaturated
release. Experiments are given to demonstrate that TBO reduces the queue length
and delay by 13.78% and 14.08% on average, respectively, compared to the
existing baselines. Furthermore, we calculate the Gini coefficients of the
right-of-way to indicate TBO does not harm fairness while improving efficiency.
