arXiv Machine Learning August 18, 2026 By Alessandro Barro

Pointer Networks with Q-Learning for Combinatorial Optimization

Read the original on arXiv Machine Learning →

arXiv:2311. 02629v5 Announce Type: replace Abstract: We introduce the Pointer Q-Network (PQN), a hybrid neural architecture that integrates model-free Q-value policy approximation with Pointer Networks (Ptr-Nets) to enhance the optimality of attention-based sequence generation, focusing on long-term outcomes.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

Jul 28

Understanding Human-like Solutions in Combinatorial Optimization via Learning and Search

arXiv:2607. 23854v1 Announce Type: new Abstract: Humans often find good solutions to combinatorial optimization problems that are computationally hard even for advanced computer algorithms.

By Haijiang Yan, Jian-Qiao Zhu, Liqiang Huang, Ming Meng

reinforcement-learning fine-tuning

arXiv AI

Jul 7

Graph Neural Networks are Heuristics

arXiv:2601. 13465v4 Announce Type: replace Abstract: Graph neural networks are usually treated as auxiliaries for combinatorial optimization: they imitate algorithms, guide search, or supply scores to classical procedures.

By Yimeng Min, Carla P. Gomes

arXiv AI

Aug 10

Aftab: A Comprehensive Benchmark of CNN Encoders and Advanced Value Functions in Parallelized Q-Networks

arXiv:2608. 07335v1 Announce Type: cross Abstract: Recent advancements in deep reinforcement learning have increasingly favored simplified, highly parallelized paradigms.

By Taha Shieenavaz, Shabnam Zareshahraki, Loris Nanni

reinforcement-learning benchmarks

arXiv Machine Learning

Jun 10

Latent Guided Sampling for Combinatorial Optimization

arXiv:2506. 03672v2 Announce Type: replace-cross Abstract: Combinatorial Optimization problems are widespread in domains such as logistics, manufacturing, and drug discovery, yet their NP-hard nature makes them computationally challenging.

By Sobihan Surendran (LPSM), Adeline Fermanian (LPSM), Sylvain Le Corff (LPSM)

diffusion reinforcement-learning benchmarks

arXiv AI

Jun 10

Model-Based Reinforcement Learning in Discrete-Action Non-Markovian Reward Decision Processes

arXiv:2512. 14617v2 Announce Type: replace-cross Abstract: Many practical decision-making problems involve tasks whose success depends on the entire system history, rather than on achieving a state with desired properties.

By Alessandro Trapasso, Luca Iocchi, Fabio Patrizi

agents reinforcement-learning benchmarks

arXiv AI

Jul 10

Spectral Analysis of Dueling Q-Learning

arXiv:2607. 08340v1 Announce Type: cross Abstract: Q-learning is a fundamental algorithm in reinforcement learning (RL) for solving discounted Markov decision processes (MDPs) when the transition kernel is unknown.

By Donghwan Lee

reinforcement-learning

Related stories

Understanding Human-like Solutions in Combinatorial Optimization via Learning and Search

Graph Neural Networks are Heuristics

Aftab: A Comprehensive Benchmark of CNN Encoders and Advanced Value Functions in Parallelized Q-Networks

Latent Guided Sampling for Combinatorial Optimization

Model-Based Reinforcement Learning in Discrete-Action Non-Markovian Reward Decision Processes

Spectral Analysis of Dueling Q-Learning