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Safety Risk Assessment for Children's Products Based on Reinforcement Learning

The prediction accuracy rate of the types of multi-factor associated injuries in the risk assessment model has been tested with 122 test data, resulting in that the correct recognition rate of the injury consequences of the adjusted model was about 75%, the identification accuracy rate of two kinds of risk factor-related events in the categories of the mechanical injuries and inorganic poison hazards reached 78%, and that of the other eight categories of risk factor-related hazard events was higher than 75%, which confirms that the test results of the risk assessment model are satisfying. After reinforcement learning, the test accuracy of the strong classifier has been improved to 81%, indicating that the reinforcement learning module has increased the generalization ability of the model at a certain extent, and the Bayesian network method combined with reinforcement learning may have stronger applicability. In the structure learning, the conditional probabilities as shown in Table 5, Table 6, and Table 7 are obtained, and the relationship that different factors cause a certain hazard is revealed, consequently to confirm how the key factors of a certain injury that is occurred.