Deep Reinforcement Learning for Join Order Enumeration

Join order selection plays a significant role in query performance. However, modern query optimizers typically employ static join enumeration algorithms that do not receive any feedback about the quality of the resulting plan. Hence, optimizers often repeatedly choose the same bad plan, as they do not have a mechanism for "learning from their mistakes". In this paper, we argue that existing deep reinforcement learning techniques can be applied to address this challenge. These techniques, powered by artificial neural networks, can automatically improve decision making by incorporating feedback from their successes and failures. Towards this goal, we present ReJOIN, a proof-of-concept join enumerator, and present preliminary results indicating that ReJOIN can match or outperform the PostgreSQL optimizer in terms of plan quality and join enumeration efficiency

The Cowl - v.38 - n.12 - Nov 18, 1983

The Cowl - student newspaper of Providence College. Vol 38 - No. 12 - November 18, 1983. 12 pages

Unique Equilibrium in Two-Part Tariff Competition between Two-Sided Platforms

Two-sided market models in which platforms compete via two-part tariffs, i.e. a subscription and a per-transaction fee, are often plagued by a continuum of equilibria. This paper augments existing models by allowing for heterogeneous rading behavior of agents on both sides. We show that this simple method yields a unique equilibrium even in the limit as the heterogeneity vanishes. In case of competitive bottlenecks we find that in this equilibrium platforms benefit from the possibility to price discriminate if per-transaction costs are relatively large. This is the case because two-part tariffs allow platforms to better distribute these costs among the two sides. Under two-sided single-homing price discrimination hurts platforms if per-transaction fees can be negative

Learning to solve planning problems efficiently by means of genetic programming

Declarative problem solving, such as planning, poses interesting challenges for Genetic Programming (GP). There have been recent attempts to apply GP to planning that fit two approaches: (a) using GP to search in plan space or (b) to evolve a planner. In this article, we propose to evolve only the heuristics to make a particular planner more efficient. This approach is more feasible than (b) because it does not have to build a planner from scratch but can take advantage of already existing planning systems. It is also more efficient than (a) because once the heuristics have been evolved, they can be used to solve a whole class of different planning problems in a planning domain, instead of running GP for every new planning problem. Empirical results show that our approach (EVOCK) is able to evolve heuristics in two planning domains (the blocks world and the logistics domain) that improve PRODIGY4.0 performance. Additionally, we experiment with a new genetic operator - Instance-Based Crossover - that is able to use traces of the base planner as raw genetic material to be injected into the evolving population.Publicad