Learning More Complex Actions with Deep Reinforcement Learning
Chenxi Wang, Youtian Du, Shengyuan Xie, Yongdi Lu · 2021
How to learn more complex actions based on the predefined atomic actions is a significant problem for improving the intelligence of machines. In this paper, we present a novel approach called action space expanding Q-learning (ASE-Q) to the learning of more complex actions. We first introduce two types of formation of the complex actions; then, we design an action combination network to dynamically learn the complex actions a Q-network to evaluate action-value of the complex actions. Learning more complex actions can speed up the process that an agent accomplishes a task and improve its capacity in learning new actions.