Performance comparison of the quantum and classical deep Q-learning approaches in dynamic environments control

There is a lack of adequate studies on dynamic environments control for Quantum Reinforcement Learning (QRL) algorithms, representing a significant gap in this field. This study contributes to bridging this gap by demonstrating the potential of quantum RL algorithms to effectively handle dynamic environments. In this research, the performance and robustness of Quantum Deep Q-learning Networks (DQN) were examined in two dynamic environments, Cart Pole and Lunar Lander, by using three distinct quantum Ansatz layers: RealAmplitudes, EfficientSU2, and TwoLocal. The quantum DQNs were compared with classical DQN algorithms in terms of convergence speed, loss minimization, and Q-value behavior. It was observed that the RealAmplitudes Ansatz outperformed the other quantum circuits, demonstrating faster convergence and superior performance in minimizing the loss function. To assess robustness, the pole length was increased in the Cart Pole environment, and a wind function was added to the Lunar Lander environment after the 50th episode. All three quantum Ansatz layers were found to maintain robust performance under disturbed conditions, with consistent reward values, loss minimization, and stable Q-value distributions. Although the proposed QRL demonstrates competitive results overall, classical RL can surpass them in convergence speed under specific conditions.