Robustness Assessment of Asynchronous Advantage Actor-Critic Based on Dynamic Skewness and Sparseness Computation: A Parallel Computing View

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%.

Automated summary

This paper conducts the first robustness assessment of A3C based on parallel computing, proposing static and dynamic methods to measure robustness. Experimental results demonstrate that the proposed robustness assessment can effectively gauge the robustness of A3C with an accuracy of 83.3%.