This paper introduces QTRL (Quantum-Train), a practical quantum reinforcement learning approach that leverages quantum neural networks (QNNs) to generate parameters for classical policy models. QTRL addresses key challenges in quantum reinforcement learning (QRL), such as data encoding and reliance on quantum hardware during inference. By using a quantum machine learning model with polylogarithmic parameter reduction, QTRL eliminates the need for complex data encoding and reduces the training parameters of the classical policy network. The resulting model is purely classical, enabling efficient inference on classical computers, which is crucial for real-world applications requiring low-latency feedback. The QTRL framework combines a quantum neural network with a mapping model to generate classical parameters. This approach allows for the training of a classical policy network using a quantum circuit, with the quantum parameters influencing the classical model through the quantum state preparation and measurement processes. The framework is designed to be compatible with classical transfer learning and fine-tuning, making it highly practical for real-world applications. Experiments on the CartPole-v1 and MiniGrid-Empty-5x5-v0 environments demonstrate that QTRL can achieve performance comparable to or better than classical methods while using significantly fewer parameters. This efficiency makes QTRL particularly suitable for reinforcement learning tasks requiring low-latency feedback, such as autonomous driving. The classical inference capability of QTRL also makes it more cost-effective compared to traditional quantum reinforcement learning approaches that require access to quantum hardware. The QTRL approach is independent of quantum hardware during inference, making it highly practical for real-world applications. This method bridges the gap between quantum and classical reinforcement learning, offering a practical solution that can be implemented with existing classical infrastructure. The results highlight the potential of QTRL in various applications, providing both practical benefits and theoretical advancements in reinforcement learning.This paper introduces QTRL (Quantum-Train), a practical quantum reinforcement learning approach that leverages quantum neural networks (QNNs) to generate parameters for classical policy models. QTRL addresses key challenges in quantum reinforcement learning (QRL), such as data encoding and reliance on quantum hardware during inference. By using a quantum machine learning model with polylogarithmic parameter reduction, QTRL eliminates the need for complex data encoding and reduces the training parameters of the classical policy network. The resulting model is purely classical, enabling efficient inference on classical computers, which is crucial for real-world applications requiring low-latency feedback. The QTRL framework combines a quantum neural network with a mapping model to generate classical parameters. This approach allows for the training of a classical policy network using a quantum circuit, with the quantum parameters influencing the classical model through the quantum state preparation and measurement processes. The framework is designed to be compatible with classical transfer learning and fine-tuning, making it highly practical for real-world applications. Experiments on the CartPole-v1 and MiniGrid-Empty-5x5-v0 environments demonstrate that QTRL can achieve performance comparable to or better than classical methods while using significantly fewer parameters. This efficiency makes QTRL particularly suitable for reinforcement learning tasks requiring low-latency feedback, such as autonomous driving. The classical inference capability of QTRL also makes it more cost-effective compared to traditional quantum reinforcement learning approaches that require access to quantum hardware. The QTRL approach is independent of quantum hardware during inference, making it highly practical for real-world applications. This method bridges the gap between quantum and classical reinforcement learning, offering a practical solution that can be implemented with existing classical infrastructure. The results highlight the potential of QTRL in various applications, providing both practical benefits and theoretical advancements in reinforcement learning.