  With the continuous development of machine learning technology, major
e-commerce platforms have launched recommendation systems based on it to serve
a large number of customers with different needs more efficiently. Compared
with traditional supervised learning, reinforcement learning can better capture
the user's state transition in the decision-making process, and consider a
series of user actions, not just the static characteristics of the user at a
certain moment. In theory, it will have a long-term perspective, producing a
more effective recommendation. The special requirements of reinforcement
learning for data make it need to rely on an offline virtual system for
training. Our project mainly establishes a virtual user environment for offline
training. At the same time, we tried to improve a reinforcement learning
algorithm based on bi-clustering to expand the action space and recommended
path space of the recommendation agent.
