Reinforcement Learning

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

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

We propose a new decentralized and online reinforcement learning algorithm for vehicle routing in a more challenging unmapped environments with provable guarantees of cost improvements over greedy base policy.
[International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2024)]

A temporal and annotated-logic based simulation proxy that enables interpretable reinforcement learning
[International Conference on Semantic Computing (ICSC 2024)]