Solving the Team Orienteering Problem Using Decision Transformers
The Team Orienteering Problem (TOP) is a well-established combinatorial optimization problem with applications in logistics, tourism, and other domains. Traditional solutions often rely on heuristics, metaheuristics, or exact algorithms, but deep learning-based approaches are emerging as promising alternatives. This study explores the use of Decision Transformers (DT)—a reinforcement learning framework based on sequence modeling—to generate high-quality solutions for the TOP. By utilizing a dataset of diverse routes with varying quality levels, the objective is for the DT to learn to predict effective action sequences that maximize the reward.
Palabras clave: Team Orienteering Problem (TOP) Combinatorial Optimization Decision Transformers (DT) Reinforcement Learning