强化学习
计算机科学
稳健性(进化)
解算器
加速
非线性系统
继续
趋同(经济学)
增强学习
人工智能
并行计算
物理
程序设计语言
化学
经济
基因
量子力学
生物化学
经济增长
作者
Zhou Jin,Haojie Pei,Yichao Dong,Xiang Jin,Xiao Ming Wu,Wei Xing,Dan Niu
标识
DOI:10.1145/3489517.3530512
摘要
DC analysis is the foundation for nonlinear electronic circuit simulation. Pseudo transient analysis (PTA) methods have gained great success among various continuation algorithms. However, PTA tends to be computationally intensive without careful tuning of parameters and proper stepping strategies. In this paper, we harness the latest advancing in machine learning to resolve these challenges simultaneously. Particularly, an active learning is leveraged to provide a fine initial solver environment, in which a TD3-based Reinforcement Learning (RL) is implemented to accelerate the simulation on the fly. The RL agent is strengthen with dual agents, priority sampling, and cooperative learning to enhance its robustness and convergence. The proposed algorithms are implemented in an out-of-the-box SPICElike simulator, which demonstrated a significant speedup: up to 3.1X for the initial stage and 234X for the RL stage.
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