  The recent outbreak of COVID-19 poses a serious threat to people's lives.
Epidemic control strategies have also caused damage to the economy by cutting
off humans' daily commute. In this paper, we develop an Individual-based
Reinforcement Learning Epidemic Control Agent (IDRLECA) to search for smart
epidemic control strategies that can simultaneously minimize infections and the
cost of mobility intervention. IDRLECA first hires an infection probability
model to calculate the current infection probability of each individual. Then,
the infection probabilities together with individuals' health status and
movement information are fed to a novel GNN to estimate the spread of the virus
through human contacts. The estimated risks are used to further support an RL
agent to select individual-level epidemic-control actions. The training of
IDRLECA is guided by a specially designed reward function considering both the
cost of mobility intervention and the effectiveness of epidemic control.
Moreover, we design a constraint for control-action selection that eases its
difficulty and further improve exploring efficiency. Extensive experimental
results demonstrate that IDRLECA can suppress infections at a very low level
and retain more than 95% of human mobility.
