  A multi-agent deep reinforcement learning-based framework for traffic
shaping. The proposed framework offers a key advantage over existing congestion
management strategies which is the ability to mitigate hysteresis phenomena.
Unlike existing congestion management strategies that focus on breakdown
prevention, the proposed framework is extremely effective after breakdown
formation. The proposed framework assumes partial connectivity between the
automated vehicles which share information. The framework requires a basic
level of autonomy defined by one-dimensional longitudinal control. This
framework is primarily built using a centralized training, centralized
execution multi-agent deep reinforcement learning approach, where longitudinal
control is defined by signals of acceleration or deceleration commands which
are then executed by all agents uniformly. The model undertaken for training
and testing of the framework is based on the well-known Double Deep Q-Learning
algorithm which takes the average state of flow within the traffic stream as
the model input and outputs actions in the form of acceleration or deceleration
values. We demonstrate the ability of the model to shape the state of traffic,
mitigate the negative effects of hysteresis, and even improve traffic flow
beyond its original level. This paper also identifies the minimum percentage of
CAVs required to successfully shape the traffic under an assumption of
uniformly distributed CAVs within the loop system. The framework illustrated in
this work doesnt just show the theoretical applicability of reinforcement
learning to tackle such challenges but also proposes a realistic solution that
only requires partial connectivity and continuous monitoring of the average
speed of the system, which can be achieved using readily available sensors that
measure the speeds of vehicles in reasonable proximity to the CAVs.
