We demonstrate that deep reinforcement learning (deep RL) provides a highly effective strategy for the control and self-tuning of optical systems. Deep RL integrates the two leading machine learning architectures of deep neural networks and reinforcement learning to produce robust and stable learning for control. Deep RL is ideally suited for optical systems as the tuning and control relies on interactions with its environment with a goal-oriented objective to achieve optimal immediate or delayed rewards. This allows the optical system to recognize bi-stable structures and navigate, via trajectory planning, to optimally performing solutions, the first such algorithm demonstrated to do so in optical systems. We specifically demonstrate the deep RL architecture on a mode-locked laser, where robust self-tuning and control can be established through access of the deep RL agent to its waveplates and polarizers. We further integrate transfer learning to help the deep RL agent rapidly ...

中文翻译：

---

光学系统的深度强化学习：锁模激光器的案例研究

我们证明了深度强化学习（深度RL）为光学系统的控制和自调整提供了一种高效的策略。Deep RL集成了深度神经网络和强化学习的两种领先的机器学习架构，以产生强大而稳定的控制学习。Deep RL非常适合光学系统，因为调节和控制依赖于与目标环境的相互作用，以实现最佳的即时或延迟回报。这允许光学系统识别双稳态结构，并通过轨迹规划导航到最佳执行解决方案，这是第一个在光学系统中证明可以这样做的算法。我们专门展示了锁模激光器上的深层RL结构，通过使用深层RL代理对其波片和偏振片的访问，可以建立强大的自调谐和控制功能。我们进一步整合了转移学习，以帮助深度RL代理快速...