深層・リカレント構造による最適制御の探求
Exploring Deep and Recurrent Architectures for Optimal Control
深層・リカレントニューラルネットを連続高次元の歩行制御に適用し、関節角からトルクへの制御則を強化学習で訓練して複数構造を比較した。
著者: Sergey Levine
分類: cs.LG, cs.AI, cs.NE, cs.RO, cs.SY, eess.SY
原文アブストラクト
Sophisticated multilayer neural networks have achieved state of the art results on multiple supervised tasks. However, successful applications of such multilayer networks to control have so far been limited largely to the perception portion of the control pipeline. In this paper, we explore the application of deep and recurrent neural networks to a continuous, high-dimensional locomotion task, where the network is used to represent a control policy that maps the state of the system (represented by joint angles) directly to the torques at each joint. By using a recent reinforcement learning algorithm called guided policy search, we can successfully train neural network controllers with thousands of parameters, allowing us to compare a variety of architectures. We discuss the differences between the locomotion control task and previous supervised perception tasks, present experimental results comparing various architectures, and discuss future directions in the application of techniques from deep learning to the problem of optimal control.
