  Active Traffic Management strategies are often adopted in real-time to
address such sudden flow breakdowns. When queuing is imminent, Speed
Harmonization (SH), which adjusts speeds in upstream traffic to mitigate
traffic showckwaves downstream, can be applied. However, because SH depends on
driver awareness and compliance, it may not always be effective in mitigating
congestion. The use of multiagent reinforcement learning for collaborative
learning, is a promising solution to this challenge. By incorporating this
technique in the control algorithms of connected and autonomous vehicle (CAV),
it may be possible to train the CAVs to make joint decisions that can mitigate
highway bottleneck congestion without human driver compliance to altered speed
limits. In this regard, we present an RL-based multi-agent CAV control model to
operate in mixed traffic (both CAVs and human-driven vehicles (HDVs)). The
results suggest that even at CAV percent share of corridor traffic as low as
10%, CAVs can significantly mitigate bottlenecks in highway traffic. Another
objective was to assess the efficacy of the RL-based controller vis-\`a-vis
that of the rule-based controller. In addressing this objective, we duly
recognize that one of the main challenges of RL-based CAV controllers is the
variety and complexity of inputs that exist in the real world, such as the
information provided to the CAV by other connected entities and sensed
information. These translate as dynamic length inputs which are difficult to
process and learn from. For this reason, we propose the use of Graphical
Convolution Networks (GCN), a specific RL technique, to preserve information
network topology and corresponding dynamic length inputs. We then use this,
combined with Deep Deterministic Policy Gradient (DDPG), to carry out
multi-agent training for congestion mitigation using the CAV controllers.
