  This paper aims to solve the coordination of a team of robots traversing a
route in the presence of adversaries with random positions. Our goal is to
minimize the overall cost of the team, which is determined by (i) the
accumulated risk when robots stay in adversary-impacted zones and (ii) the
mission completion time. During traversal, robots can reduce their speed and
act as a `guard' (the slower, the better), which will decrease the risks
certain adversary incurs. This leads to a trade-off between the robots'
guarding behaviors and their travel speeds. The formulated problem is highly
non-convex and cannot be efficiently solved by existing algorithms. Our
approach includes a theoretical analysis of the robots' behaviors for the
single-adversary case. As the scale of the problem expands, solving the optimal
solution using optimization approaches is challenging, therefore, we employ
reinforcement learning techniques by developing new encoding and
policy-generating methods. Simulations demonstrate that our learning methods
can efficiently produce team coordination behaviors. We discuss the reasoning
behind these behaviors and explain why they reduce the overall team cost.
