This paper examines the problem of establishing a pricing policy that maximizes the revenue for selling a given inventory by a fixed deadline. This problem is faced by a variety of industries, including airlines, hotels and fashion. Reinforcement learning algorithms are used to analyze how firms...

Šio tyrimo objektas yra akcijų rinka, kuri suvokiama kaip kompleksinė sistema, sudaryta iš bazinių elementų (vertybinių popierių, prekybos infrastruktūros ir atomistinių heterogeninių investuotojų) ir procesų (prognozavimo, investicinių sprendimų priėmimo, finansinių sąskaitų...

The main object of this study is the stock market, seen as a complex system constituted of basic elements (securities, trading infrastructure and atomistic heterogeneous investors) and process flows (forecasting, investment decision making, trade execution, maintenance of financial records,...

The advent of reinforcement learning (RL) in financial markets is driven by several advantages inherent to this field of artificial intelligence. In particular, RL allows to combine the "prediction" and the "portfolio construction" task in one integrated step, thereby closely aligning the...

A novel debate within competition policy and regulation circles is whether autonomous machine learning algorithms may learn to collude on prices. We show that when firms face short-run price commitments, independent Q-learning (a simple but well-established self-learning algorithm) learns to...