Policy Synthesis for Finite Populations of MDP Agents under Aggregate Reach-Avoid Chance Constraints
This research develops a method to find optimal control policies for groups of agents moving in a discrete environment while ensuring probabilistic safety. It tackles chance constraints—requirements that reaching a target or hitting an unsafe zone must happen with certain probabilities—by using Sequential Convex Approximation to optimize density-feedback strategies. The result is a policy synthesis framework that works well for large populations.