Evaluating Correctness of Reinforcement Learning based on Actor-Critic Algorithm

Deep learning is used for decision making and functional control in various fields, such as autonomous systems. However, rather than being developed by logical design, deep learning models are trained by itself through learning data. Moreover, only reward values are used to evaluate its performance, which does not provide enough information that the model learned properly. This paper proposes a new method to assess the correctness of reinforcement learning, considering other properties of the learning algorithm. The proposed method is applied for the evaluation of Actor-Critic Algorithms, and correctness-related insights of the algorithm are confirmed through experiments.