  Modeling cell interactions such as co-attraction and contact-inhibition of
locomotion is essential for understanding collective cell migration. Here, we
propose a novel deep reinforcement learning model for collective neural crest
cell migration. We apply the deep deterministic policy gradient algorithm in
association with a particle dynamics simulation environment to train agents to
determine the migration path. Because of the different migration mechanisms of
leader and follower neural crest cells, we train two types of agents (leaders
and followers) to learn the collective cell migration behavior. For a leader
agent, we consider a linear combination of a global task, resulting in the
shortest path to the target source, and a local task, resulting in a
coordinated motion along the local chemoattractant gradient. For a follower
agent, we consider only the local task. First, we show that the self-driven
forces learned by the leader cell point approximately to the placode, which
means that the agent is able to learn to follow the shortest path to the
target. To validate our method, we compare the total time elapsed for agents to
reach the placode computed using the proposed method and the time computed
using an agent-based model. The distributions of the migration time intervals
calculated using the two methods are shown to not differ significantly. We then
study the effect of co-attraction and contact-inhibition of locomotion to the
collective leader cell migration. We show that the overall leader cell
migration for the case with co-attraction is slower because the co-attraction
mitigates the source-driven effect. In addition, we find that the leader and
follower agents learn to follow a similar migration behavior as in experimental
observations. Overall, our proposed method provides useful insight on how to
apply reinforcement learning techniques to simulate collective cell migration.
