Conventional laser systems have disadvantages such as high energy loss and low transmission efficiency, which need to be optimized and improved by new methods. In this paper, a deep reinforcement learning (RL)-based energy transmission loss suppression and efficiency optimization method for high-power laser systems is proposed. First, the attenuation mechanism of laser transmission in the atmosphere is analyzed and the corresponding thermodynamic model is established. Then, the A-TD3 algorithm in deep reinforcement learning is used to optimize the energy transmission efficiency of the laser system. Simulation results show that the A-TD3 algorithm has better convergence under different learning rates, and the algorithm converges within 150 rounds at a learning rate of 0.0005 and improves the average energy transfer efficiency of the laser system to 9.7. Compared with the traditional algorithms (e.g., DQN, DDPG, and TD3), the A-TD3 algorithm has faster convergence speed and higher transfer efficiency (9.3 vs. 9.7). In addition, the energy transfer loss of the system is optimized to reduce up to 30%-70% compared to the unoptimized system. These results demonstrate the potential application of deep reinforcement learning in the optimization of high-power laser systems. By this method, not only the loss in the transmission process can be effectively reduced, but also the overall efficiency of laser energy transmission can be improved.
