Бібліографічний опис:
Prysiazhnyk, Andrii. Dynamic Pricing using Reinforcement Learning for the Amazon marketplace: Bachelor Thesis: manuscript / Andrii Prysiazhnyk; Supervisor: PhD Taras Firman; Ukrainian Catholic University, Department of Computer Sciences. – Lviv 2021. – 54 p.: ill.
Короткий опис (реферат):
This thesis proposes and compares a few approaches for tackling the dynamic pricing
problem for e-commerce platforms. Dynamic pricing engines may help e-retailers
to increase their performance indicators and gain useful market insights. We worked
with the Amazon marketplace, using customer sales data along with additional data
from the Amazon services. Demand forecasting-based and RL-based pricing strategies
were considered. We gave a detailed explanation of each method, commenting
on its pros and cons. In order to train RL agents and compare them with baseline
methods, the simulator of the market environment was built. Conducted experiments
proved the effectiveness and advantages of RL-based methods over the classic
approaches. We also propose the idea for future works on how RL-based pricing
could be further enhanced. The source code of our study is publicly available on
GitHub.
