Virtual-Taobao: Virtualizing Real-world Online Retail Environment for
  Reinforcement Learning
  Applying reinforcement learning in physical-world tasks is extremely
challenging. It is commonly infeasible to sample a large number of trials, as
required by current reinforcement learning methods, in a physical environment.
This paper reports our project on using reinforcement learning for better
commodity search in Taobao, one of the largest online retail platforms and
meanwhile a physical environment with a high sampling cost. Instead of training
reinforcement learning in Taobao directly, we present our approach: first we
build Virtual Taobao, a simulator learned from historical customer behavior
data through the proposed GAN-SD (GAN for Simulating Distributions) and MAIL
(multi-agent adversarial imitation learning), and then we train policies in
Virtual Taobao with no physical costs in which ANC (Action Norm Constraint)
strategy is proposed to reduce over-fitting. In experiments, Virtual Taobao is
trained from hundreds of millions of customers' records, and its properties are
compared with the real environment. The results disclose that Virtual Taobao
faithfully recovers important properties of the real environment. We also show
that the policies trained in Virtual Taobao can have significantly superior
online performance to the traditional supervised approaches. We hope our work
could shed some light on reinforcement learning applications in complex
physical environments.
