Pointer Networks with Q-Learning for OP Combinatorial Optimization

The Orienteering Problem (OP) presents a unique challenge in combinatorial optimization, emphasized by its widespread use in logistics, delivery, and transportation planning. Given the NP-hard nature of OP, obtaining optimal solutions is inherently complex. While Pointer Networks (Ptr-Nets) have exhibited prowess in various combinatorial tasks, their performance in the context of OP leaves room for improvement. Recognizing the potency of Q-learning, especially when paired with deep neural structures, this research unveils the Pointer Q-Network (PQN). This innovative method combines Ptr-Nets and Q-learning, effectively addressing the specific challenges presented by OP. We deeply explore the architecture and efficiency of PQN, showcasing its superior capability in managing OP situations.