Designing freeform imaging systems based on reinforcement learning

The design of complex freeform imaging systems with advanced system specification is often a tedious task that requires extensive human effort. In addition, the lack of design experience or expertise that result from the complex and uncertain nature of freeform optics, in addition to the limited history of usage, also contributes to the design difficulty. In this paper, we propose a design framework of freeform imaging systems using reinforcement learning. A trial-and-error method employing different design routes that use a successive optimization process is applied in different episodes under an ε-greedy policy. An "exploitation-exploration, evaluation and back-up" approach is used to interact with the environment and discover optimal policies. Design results with good imaging performance and related design routes can be found automatically. The design experience can be further summarized using the obtained data directly or through other methods such as clustering-based machine learning. The experience offers valuable insight for completing other related design tasks. Human effort can be significantly reduced in both the design process and the tedious process of summarizing experience. This design framework can be integrated into optical design software and runs nonstop in the background or on servers to complete design tasks and acquire experience automatically for various types of systems.