Projects

A new decentralized and online reinforcement learning algorithm for vehicle routing problem in unmapped environments

Using genetic algorithms to find distributed local algorithms that generate global collective behaviors

Our team at LabV2(ASU) [collaboration with Leidos Inc.] works on modeling agent movements in real-world maps. We use knowledge graphs to model the maps in combination with symbolic rule learning and heuristic based graph traversal algorithms to model and learn agent trajectories(both anamolous and normal).

A temporal and annotated-logic based simulation proxy that enables interpretable reinforcement learning

Explores evolutionary algorithms to build neural network robot controllers for building collective behaviors