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Inverse design of the transmission matrix in a random system using Reinforcement Learning

arXiv 2506.13057 ↗First posted 16 Jun 2025physics.optics cond-mat.dis-nn cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation

Abstract (arXiv)

This work presents an approach to the inverse design of scattering systems by modifying the transmission matrix using reinforcement learning. We utilize Proximal Policy Optimization to navigate the highly non-convex landscape of the object function to achieve three types of transmission matrices: (1) Fixed-ratio power conversion and zero-transmission mode in rank-1 matrices, (2) exceptional points with degenerate eigenvalues and unidirectional mode conversion, and (3) uniform channel participation is enforced when transmission eigenvalues are degenerate.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 5 of its 37 references that are in the map. First posted on arXiv 16 Jun 2025.

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5 of 37 references.

References · 5

  1. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  2. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  3. 2022Reinforcement Learning Algorithms: An Overview and Classificationcited by 10
  4. 2020Deep reinforcement learning for optical systems: A case study of mode-locked laserscited by 5
  5. 2021Inverse Design of Grating Couplers Using the Policy Gradient Method from Reinforcement Learningcited by 3

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  4. 2026Data-Driven Inverse Reinforcement Learning of Linear Systems with Model Uncertainty: A Convex Optimization View
  5. 2026Inverse Optimal Control for Linear Quadratic Problem with Poisson Jumps: Model-Free Inverse Reinforcement Learning Approaches
  6. 2024On Reward Transferability in Adversarial Inverse Reinforcement Learning: Insights from Random Matrix Theory
  7. 2024Reinforcement Learning of Adaptive Acquisition Policies for Inverse Problems
  8. 2025Policy-Based Radiative Transfer: Solving the 2-Level Atom Non-LTE Problem using Soft Actor-Critic Reinforcement Learning