Internet of Things equipment detection and control method based on deep reinforcement learning

The invention relates to the technical field of Internet of Things data acquisition, and discloses an Internet of Things equipment detection and control method based on deep reinforcement learning, which comprises the following steps: 1, constructing a multi-dimensional equipment state set and a strategy set, 2, updating a value function Q (s, b) corresponding to each strategy, 3, determining a reward coefficient R and a reward discount alpha, 4, Q (s, b) value neural network. 5, Q network loss function selection; 6, DQN training until the algorithm reaches a convergence condition. 本发明涉及物联网数据采集技术领域，且公开了一种基于深度强化学习的物联网设备探测与控制方法，包括以下步骤：1.构造多维设备状态集以及策略集，2.更新各策略对应的值函数Q(s,b)，3.确定奖励系数R和奖赏折扣α，4.Q(s,b)值神经网络化。5.Q网络损失函数选取，6.DQN训练，直到算法达到收敛条件。