Policy Gradient Approach of Event‐Based Optimization and Its Online Implementation

Abstract

AbstractIn the theory of event‐based optimization (EBO), the decision making is triggered by events, which is different from the traditional state‐based control in Markov decision processes (MDP). In this paper, we propose a policy gradient approach of EBO. First, an equation of performance gradient in the event‐based policy space is derived based on a fundamental quantity called Q‐factors of EBO. With the performance gradient, we can find the local optimum of EBO using the gradient‐based algorithm. Compared to the policy iteration approach in EBO, this policy gradient approach does not require restrictive conditions and it has a wider application scenario. The policy gradient approach is further implemented based on the online estimation of Q‐factors. This approach does not require the prior information about the system parameters, such as the transition probability. Finally, we use an EBO model to formulate the admission control problem and demonstrate the main idea of this paper. Such online algorithm provides an effective implementation of the EBO theory in practice.