No abstract

Combinatorial optimization (CO) is the workhorse of numerous important applications in operations research, engineering and other fields and, thus, has been attracting enormous attention from the research community for over a century. Many efficient solutions to common problems involve using hand-crafted heuristics to sequentially construct a solution. Therefore, it is intriguing to see how a combinatorial optimization problem can be formulated as a sequential decision making process and whether efficient heuristics can be implicitly learned by a reinforcement learning agent to find a solution. This survey explores the synergy between CO and reinforcement learning (RL) framework, which can become a promising direction for solving combinatorial problems.

Reinforcement learning (RL) enjoyed significant progress over the last years. One of the most important steps forward was the wide application of neural networks. However, architectures of these neural networks are typically constructed manually. In this work, we study recently proposed neural architecture search (NAS) methods for optimizing the architecture of RL agents. We carry out experiments on the Atari benchmark and conclude that modern NAS methods find architectures of RL agents outperforming a manually selected one.

Query Optimization is considered to be one of the most important challenges in database management. Existing built-in query optimizers are very complex and rely on various approximations and hand-picked rules. The rise of deep learning and deep reinforcement learning has aided many scientific and industrial fields, providing an opportunity to develop a learnable query optimizer. In this paper, we analyse and improve the state-of-the-art learned query optimizer, Neo for the JOB benchmark on two database systems: PostgreSQL and Huawei GaussDB. We describe our methods, based on combination of Neo, Tree-Transformers, auxiliary tasks, reward weighting. Combinations of these methods improve latency of the found query execution plans. We also conduct a thorough analysis of the resulting execution plans and devise a set of decision-based rules to indicate the cases when the learned optimizer will outperform the built-in one. We also provide a source code for the proposed methods and experiments. Finally, we provide possible directions for further improvement in this field.