Epidemic Decision-making System Based Federated Reinforcement Learning
Yangxi Zhou Junping Du Zhe Xue Zhenhui Pan Weikang Chen
Epidemic decision-making can effectively help the government to
comprehensively consider public security and economic development to respond to
public health and safety emergencies. Epidemic decision-making can effectively
help the government to comprehensively consider public security and economic
development to respond to public health and safety emergencies. Some studies
have shown that intensive learning can effectively help the government to make
epidemic decision, thus achieving the balance between health security and
economic development. Some studies have shown that intensive learning can
effectively help the government to make epidemic decision, thus achieving the
balance between health security and economic development. However, epidemic
data often has the characteristics of limited samples and high privacy.
However, epidemic data often has the characteristics of limited samples and
high privacy. This model can combine the epidemic situation data of various
provinces for cooperative training to use as an enhanced learning model for
epidemic situation decision, while protecting the privacy of data. The
experiment shows that the enhanced federated learning can obtain more optimized
performance and return than the enhanced learning, and the enhanced federated
learning can also accelerate the training convergence speed of the training
model. accelerate the training convergence speed of the client. At the same
time, through the experimental comparison, A2C is the most suitable
reinforcement learning model for the epidemic situation decision-making.
learning model for the epidemic situation decision-making scenario, followed by
the PPO model, and the performance of DDPG is unsatisfactory.