Parameters Optimization for Reinforcement Learning with Nonlinear Time-Varying Strategy by Using Uniform Experiment Design

This paper optimizes reinforcement learning (RL) parameters and makes agents solve problems more accurately and efficiently. The RL algorithm that the paper used is Q-learning, and the experimental environment is mazes. There are three parameters to influence the entire performance in RL, such as learning rate, greedy factor, and discount rate. This paper introduces a nonlinear time-varying strate...