Agents for Optimization
Overview
Many industrial problems (routing, scheduling, circuit design) are NP-hard combinatorial optimization challenges. We investigate whether learning-based agents can “outsmart” or accelerate classical solvers.
Active Projects
1. Neural Combinatorial Optimization
Goal: Learning heuristics from data. Details: Instead of hand-crafting heuristics for every new problem, we train RL agents to learn construction and improvement heuristics automatically. We focus on graph-based problems where the agent learns to traverse the graph to build a valid solution.
2. Generalizable Solvers
Goal: Agents that generalize across problem sizes. Details: A major limitation of neural solvers is generalization. We are designing architectures (based on GNNs and attention) that allow an agent trained on small graphs (e.g., 20 nodes) to zero-shot generalize to large-scale instances (e.g., 1000 nodes) without retraining.