2 research outputs found
Dynamic Pricing on E-commerce Platform with Deep Reinforcement Learning
In this paper we present an end-to-end framework for addressing the problem
of dynamic pricing on E-commerce platform using methods based on deep
reinforcement learning (DRL). By using four groups of different business data
to represent the states of each time period, we model the dynamic pricing
problem as a Markov Decision Process (MDP). Compared with the state-of-the-art
DRL-based dynamic pricing algorithms, our approaches make the following three
contributions. First, we extend the discrete set problem to the continuous
price set. Second, instead of using revenue as the reward function directly, we
define a new function named difference of revenue conversion rates (DRCR).
Third, the cold-start problem of MDP is tackled by pre-training and evaluation
using some carefully chosen historical sales data. Our approaches are evaluated
by both offline evaluation method using real dataset of Alibaba Inc., and
online field experiments on Tmall.com, a major online shopping website owned by
Alibaba Inc.. In particular, experiment results suggest that DRCR is a more
appropriate reward function than revenue, which is widely used by current
literature. In the end, field experiments, which last for months on 1000 stock
keeping units (SKUs) of products demonstrate that continuous price sets have
better performance than discrete sets and show that our approaches
significantly outperformed the manual pricing by operation experts.Comment: 9 pages, 7 figure
Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics
The popularity of deep reinforcement learning (DRL) methods in economics have
been exponentially increased. DRL through a wide range of capabilities from
reinforcement learning (RL) and deep learning (DL) for handling sophisticated
dynamic business environments offers vast opportunities. DRL is characterized
by scalability with the potential to be applied to high-dimensional problems in
conjunction with noisy and nonlinear patterns of economic data. In this work,
we first consider a brief review of DL, RL, and deep RL methods in diverse
applications in economics providing an in-depth insight into the state of the
art. Furthermore, the architecture of DRL applied to economic applications is
investigated in order to highlight the complexity, robustness, accuracy,
performance, computational tasks, risk constraints, and profitability. The
survey results indicate that DRL can provide better performance and higher
accuracy as compared to the traditional algorithms while facing real economic
problems at the presence of risk parameters and the ever-increasing
uncertainties.Comment: 42 pages, 26 figure