A multi-agent bandit RL formulation for binary photonic topology optimization produces designs that outperform gradient-based optimization on eight simulated 2D and 3D tasks.
Crossq: Batch normalization in deep reinforcement learning for greater sample efficiency and simplicity
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Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits
A multi-agent bandit RL formulation for binary photonic topology optimization produces designs that outperform gradient-based optimization on eight simulated 2D and 3D tasks.