@article{Chen2021, 
author = {Tong Chen and Ji-Qiang Liu and He Li and Shuo-Ru Wang and Wen-Jia Niu and En-Dong Tong and Liang Chang and Qi Alfred Chen and Gang Li},
title = {Robustness Assessment of Asynchronous Advantage Actor-Critic Based on Dynamic Skewness and Sparseness Computation: A Parallel Computing View},
year = {2021},
journal = {Journal of Computer Science and Technology},
volume = {36},
number = {5},
pages = {1002-1021},
keywords = {robustness assessment, skewness, sparseness, asynchronous advantage actor-critic, reinforcement learning},
url = {https://www.sciopen.com/article/10.1007/s11390-021-1217-z},
doi = {10.1007/s11390-021-1217-z},
abstract = {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 inuence 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%.}
}