Reinforcement learning as autonomous learning is greatly driving artificial intelligence(AI)development to practical applications.Having demonstrated the potential to significantly improve synchronously parallel learn...Reinforcement learning as autonomous learning is greatly driving artificial intelligence(AI)development to practical applications.Having demonstrated the potential to significantly improve synchronously parallel learning,the parallel computing based asynchronous advantage actor-critic(A3C)opens a new door for reinforcement learning.Unfortunately,the acceleration's influence on A3C robustness has been largely overlooked.In this paper,we perform the first robustness assessment of A3C based on parallel computing.By perceiving the policy's action,we construct a global matrix of action probability deviation and define two novel measures of skewness and sparseness to form an integral robustness measure.Based on such static assessment,we then develop a dynamic robustness assessing algorithm through situational whole-space state sampling of changing episodes.Extensive experiments with different combinations of agent number and learning rate are implemented on an A3C-based pathfinding application,demonstrating that our proposed robustness assessment can effectively measure the robustness of A3C,which can achieve an accuracy of 83.3%.展开更多
基金supported by the National Natural Science Foundation of China under Grant Nos.61972025,61802389,61672092,U1811264,and 61966009the National Key Research and Development Program of China under Grant Nos.2020YFB1005604 and 2020YFB2103802Guangxi Key Laboratory of Trusted Software under Grant No.KX201902.
文摘Reinforcement learning as autonomous learning is greatly driving artificial intelligence(AI)development to practical applications.Having demonstrated the potential to significantly improve synchronously parallel learning,the parallel computing based asynchronous advantage actor-critic(A3C)opens a new door for reinforcement learning.Unfortunately,the acceleration's influence on A3C robustness has been largely overlooked.In this paper,we perform the first robustness assessment of A3C based on parallel computing.By perceiving the policy's action,we construct a global matrix of action probability deviation and define two novel measures of skewness and sparseness to form an integral robustness measure.Based on such static assessment,we then develop a dynamic robustness assessing algorithm through situational whole-space state sampling of changing episodes.Extensive experiments with different combinations of agent number and learning rate are implemented on an A3C-based pathfinding application,demonstrating that our proposed robustness assessment can effectively measure the robustness of A3C,which can achieve an accuracy of 83.3%.
