Optimal control problem of various epidemic models with uncertainty based on deep reinforcement learning

초록

We investigate an optimal control problem of various epidemic models with uncertainty using stochastic differential equations, random differential equations, and agent-based models. We discuss deep reinforcement learning (RL), which combines RL with deep neural networks, as one method to solve the optimal control problem. The deep Q-network algorithm is introduced to approximate an action-value function and consequently obtain the optimal policy. Numerical simulations show that in order to effectively prevent the spread of infectious diseases, it is essential to vaccinate at the highest rate for the first few days and then gradually reduce the rate.

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