Abstract
White-light continuum (WLC) generation in bulk media finds numerous applications in ultrafast optics and spectroscopy. Due to the complexity of the underlying spatiotemporal dynamics, WLC optimization typically follows empirical procedures. Deep reinforcement learning (RL) is a branch of machine learning dealing with the control of automated systems using deep neural networks. In this Letter, we demonstrate the capability of a deep RL agent to generate a long-term-stable WLC from a bulk medium without any previous knowledge of the system dynamics or functioning. This work demonstrates that RL can be exploited effectively to control complex nonlinear optical experiments.
Highlights
In recent years, a strong synergy has developed between photonics and the computational tools collectively referred to as artificial intelligence (AI) [1], bringing mutual benefits to both disciplines
We deepen the link between the two fields by applying reinforcement learning (RL) [7] to automate white-light continuum (WLC) generation, one of the central problems in nonlinear optics
RL is a powerful method for the solution to optimization problems that can be formalized as Markov decision processes (MDPs) [19]
Summary
A strong synergy has developed between photonics and the computational tools collectively referred to as artificial intelligence (AI) [1], bringing mutual benefits to both disciplines. Deep neural networks (NNs) [20] are powerful mathematical tools that allow the agent to understand complex environments, and learn how to obtain good rewards (see Supplement 1).
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