LensRL Initial Release

LensRL Initial Release Notes

Version: 0.1.0
Date: 2025-01-22

We are pleased to announce the initial release of LensRL, a scientific toolkit aimed at automating optical lens design through reinforcement learning (RL). This framework integrates with Optiland to provide a modular environment for optimizing optical systems using standard RL methodologies.

Key Features

Core Framework

• Gymnasium-Compatible Environment: Introduces LensDesignEnv, a standardized RL environment compliant with the OpenAI Gym API, facilitating the application of agents to optical design tasks.
• Configurable Optical System: Implements ConfigurableOptic, a dynamic class bridging RL agents with the Optiland physics engine for real-time ray tracing and analysis.

Optimization & Action Space

• Hybrid Action Space: Supports both continuous parameters and discrete architectural changes.
• Optimization Modules: Includes OptimizeRadiiAction and OptimizeAllAction for local gradient-based refinement of surface radii and thicknesses.
• Topological Updates: Provides discrete actions for structural changes, including AddLensAction, ChangeGlassAction (supporting Schott catalog), and MoveStopAction.

Reward Architecture

• Modular Reward Functions: A suite of configurable objectives to guide agent behavior:
  • RMSReward: Minimizes root-mean-square spot size.
  • ComplexityReward: Penalizes excessive optical elements to encourage parsimony.
  • ApertureFOVReward: Incentivizes larger apertures and wider fields of view.
  • CompositeReward: Allows for the weighted combination of multiple objectives.

Utilities

• Normalization Engine: Includes a robust normalization utility to scale optical parameters (e.g., radii, refractive indices) into stable ranges for neural network training.

System Requirements

• Python 3.x
• Dependencies: optiland, numpy, gymnasium.

Documentation

• See README.md for installation instructions and architecture overview.
• A minimum working example is provided in examples/minimum_working_example.ipynb.