Reinforcement learning with via-point representation

Hiroyuki Miyamoto, Jun Morimoto, Kenji Doya, Mitsuo Kawato
2004 Neural Networks  
In this paper, we propose a new learning framework for motor control. This framework consists of two components: reinforcement learning and via-point representation. In the field of motor control, conventional reinforcement learning has been used to acquire control sequences such as cart-pole or stand-up robot control. Recently, researchers have become interested in hierarchical architecture, such as multiple levels, and multiple temporal and spatial scales. Our new framework contains two
more » ... of hierarchical architecture. The higher level is implemented using via-point representation, which corresponds to macro-actions or multiple time scales. The lower level is implemented using a trajectory generator that produces primitive actions. Our framework can modify the ongoing movement by means of temporally localized via-points and trajectory generation. Successful results are obtained in computer simulation of the cart-pole swing up task. q
doi:10.1016/j.neunet.2003.11.004 pmid:15037348 fatcat:ax2o2aupuvcqtg53g62qkzvnwu