The soft actor–critic algorithm for automatic mode-locked fiber lasers

Abstract

With the development of artificial intelligence, deep reinforcement learning (DRL) has been applied for fiber laser. In this paper, the intelligent passively mode-locked fiber laser (PMLFL) with the Soft Actor–Critic (SAC) algorithm is reported. The SAC algorithm is a DRL algorithm with random policy, which combines the Actor–Critic framework and the maximum entropy. The agent learns the logic of mode-locking by outputting actions and inputting states of the laser. Due to the maximum entropy model, more exploration is encouraged, which means that multiple policies can be learned to maximize the reward, and the robustness is enhanced accordingly. The results show that the logic learned by the agent is similar to that of human. In 80 random initial state of polarization mode-locked tests, 37 explorations are needed on average, and the frequency of achieving mode-locked state exceeds 0.8 within 60 explorations. Further, the laser system can be monitored or controlled remotely, which expands the application scenarios.