Optimization of Civic Education Teaching Strategies Based on Reinforcement Learning and Its Numerical Simulation

Driven by the core qualities of the Civics discipline, the requirements of curriculum reform and the needs of teaching practice, the optimization of teaching strategies has become particularly urgent in the field of Civics education.The article introduces the Markov decision-making process and basic elements of reinforcement learning, combines the Q learning algorithm with neural networks, and constructs a deep reinforcement learning model (IDQN) for multiple intelligences with collaborative scheduling.Based on this, a numerical simulation experiment of deep reinforcement learning strategy in Civics teaching was designed and implemented.Through experimental analysis: when the recommended path is 30, the IDQN model has the best learning path recommendation effect, with an IKL of 0.477.The model also has excellent performance in the allocation of teaching resources, with the accuracy, recall and F1 value of 5 tests above 90%.After the numerical simulation of Civic Education teaching, the learning interest, attitude, and motivation of students in the experimental group increased by 27.52% to 34.49%.Under this influence, combined with the learning path and resource allocation provided by the IDQN model, students in the experimental group showed a significant improvement in their learning effect, and the average score of Civic Education Theory was 6.06 points higher than that of the control group.