  Reliability quantification of deep reinforcement learning (DRL)-based control
is a significant challenge for the practical application of artificial
intelligence (AI) in safety-critical systems. This study proposes a method for
quantifying the reliability of DRL-based control. First, an existing method,
random noise distillation, was applied to the reliability evaluation to clarify
the issues to be solved. Second, a novel method for reliability quantification
was proposed to solve these issues. The reliability is quantified using two
neural networks: reference and evaluator. They have the same structure with the
same initial parameters. The outputs of the two networks were the same before
training. During training, the evaluator network parameters were updated to
maximize the difference between the reference and evaluator networks for
trained data. Thus, the reliability of the DRL-based control for a state can be
evaluated based on the difference in output between the two networks. The
proposed method was applied to DQN-based control as an example of a simple
task, and its effectiveness was demonstrated. Finally, the proposed method was
applied to the problem of switching trained models depending on the state.
Con-sequently, the performance of the DRL-based control was improved by
switching the trained models according to their reliability.
