A deep reinforcement learning method for structural dominant failure modes searching based on self-play strategy

In the research area of structural reliability analysis (SRA), the dominant failure modes (DFMs) of a structural system make significant contributions to life-span failure prediction and safety assessment. However, the high computational cost caused by the combinatorial explosion is the main problem in DFMs searching that hinders its application and further development. Recently, many successful applications have proved that the self-play deep reinforcement learning (DRL) has a strong ability to obtain action policy in the face of combinatorial explosion problems. Inspired by this, a self-play strategy is designed to optimize the DRL-based DFMs searching process and reduce the computational effort. A scoring function is designed and used as the refereeing standard of the self-play games and helps improve the efficiency of Monte Carlo tree search (MCTS) in an asynchronous training process. In comparison with the beta-unzipping method and exploration-based DFMs searching method, the pro-posed method significantly improved training efficiency with an accuracy of over 95% and a lower requirement of the number of finite element analysis (FEA), both of which contribute to the policy learning of failure component selection. In summary, the method shows potential applications for actual structures and makes valuable contributions to the problem with high computing costs.

Automated summary

In this study, a self-play strategy based on deep reinforcement learning (DRL) is proposed to optimize the search process for dominant failure modes (DFMs) in structural reliability analysis. The method significantly improves training efficiency compared to other methods, achieving an accuracy of over 95% and requiring fewer finite element analyses (FEA).