کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
1032740 1483679 2014 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Real-time dynamic pricing in a non-stationary environment using model-free reinforcement learning
ترجمه فارسی عنوان
قیمت گذاری پویا در زمان واقعی در یک محیط غیر ثابت با استفاده از یادگیری تقویتی بدون مدل
کلمات کلیدی
موضوعات مرتبط
علوم انسانی و اجتماعی مدیریت، کسب و کار و حسابداری استراتژی و مدیریت استراتژیک
چکیده انگلیسی


• Use reinforcement learning, Q-learning and Q-learning with eligibility traces algorithms, to learn and optimize pricing strategies.
• Present an approach that avoids optimization errors caused by the underlying model.
• Compare a model-free approach with a parameterized structure approach.
• Show that when using reinforcement learning, the problem of inter-dependent demand can be modelled without increasing computational complexity.

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 can both learn and optimize their pricing strategies while interacting with their customers. We show that by using reinforcement learning we can model the problem with inter-dependent demands. This type of model can be useful in producing a more accurate pricing scheme of services or products when important events affect consumer preferences. This paper proposes a methodology to optimize revenue in a model-free environment in which demand is learned and pricing decisions are updated in real-time. We compare the performance of the learning algorithms using Monte-Carlo simulation.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Omega - Volume 47, September 2014, Pages 116–126
نویسندگان
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