AutoREC: A reinforcement learning platform for equivalent circuit model generation

arXiv:2604.27266 · cs.LG, cond-mat.mtrl-sci · Submitted 2026-04-29 · Read on arXiv

cs.LG, cond-mat.mtrl-sci

Submitted: 2026-04-29

Updated: 2026-08-30

Terminology

• Action Space — 12× in this paper · explained in 1 episode
• Reinforcement Learning — 8× in this paper · explained in 4 episodes
• Generalization — 5× in this paper · explained in 13 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Interpretability — 3× in this paper · explained in 15 episodes
• Reinforcement Learning (RL) — 3× in this paper · explained in 25 episodes
• Reward Function — 3× in this paper · explained in 2 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Core — 2× in this paper · explained in 2 episodes
• Physical Constraints — 2× in this paper · explained in 7 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Tree-structured Parzen Estimator — 2× in this paper
• Activation Function — 1× in this paper · explained in 1 episode
• Bayesian Inference — 1× in this paper · explained in 5 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Gene Expression Programming — 1× in this paper
• Modular Architecture — 1× in this paper · explained in 4 episodes
• Parsimony — 1× in this paper · explained in 2 episodes
• Q-learning — 1× in this paper · explained in 3 episodes
• Sample Efficiency — 1× in this paper · explained in 4 episodes

Sources

• Playing Atari with Deep Reinforcement Learning
• Prioritized Experience Replay
• UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
• Optuna: A Next-generation Hyperparameter Optimization Framework
• Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance

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