The quantum neural network represents a fusion of neural network algorithms and quantum computing techniques. Neural network algorithms form the foundation, with implementations typically relying on PyTorch. Similarly, software frameworks for quantum computing, such as Qiskit, are predominantly Python-based. In our work, we focus on parameterized quantum circuits, primarily employing quantum computing simulators. These parameterized quantum circuits serve as the building blocks for our neural networks, replacing traditional MLP (Multi-Layer Perceptron) networks responsible for decision-making in reinforcement learning algorithms.The optimization of our quantum neural network is accomplished through the Adam optimizer, featuring a loss function rooted in the L1-parameter implementation of measured and true values. The initial learning rate is set at 0.001.