Hierarchical Intermittent Motor Control With Deterministic Policy Gradient
Haibo Shi, Yaoru Sun, Guangyuan Li, Fang Wang, Daming Wang, Jie Li · IEEE Access · 2019
It has been evidenced that the neural motor control exploits the hierarchical and intermittent representation. In this paper, we propose a hierarchical deep reinforcement learning (DRL) method to learn the continuous control policy across multiple levels, by unifying the neuroscience principle of the minimum transition hypothesis. The control policies in the two levels of the hierarchy operate at different time scales. The high-level controller produces the intermittent actions to set a sequence of goals for the low-level controller, which in turn conducts the basic skills with the modulation of goals. The goal planning and the basic motor skills are trained jointly with the proposed algorithm: hierarchical intermittent deep deterministic policy gradient (HI-DDPG). The performance of the method is validated in two continuous control problems. The results show that the method successfully learns to temporally decompose compound tasks into sequences of basic motions with sparse transitions and outperforms the previous DRL methods that lack a hierarchical continuous representation.