深層強化学習による多地形ロボットのフリッパー連続制御
Towards continuous control of flippers for a multi-terrain robot using deep reinforcement learning
DDPGを用いて、フリッパー付きクローラロボットのフリッパー角度を連続的に制御するエンドツーエンドの強化学習アルゴリズムを開発した。
著者: Giuseppe Paolo, Lei Tai, Ming Liu
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
In this paper we focus on developing a control algorithm for multi-terrain tracked robots with flippers using a reinforcement learning (RL) approach. The work is based on the deep deterministic policy gradient (DDPG) algorithm, proven to be very successful in simple simulation environments. The algorithm works in an end-to-end fashion in order to control the continuous position of the flippers. This end-to-end approach makes it easy to apply the controller to a wide array of circumstances, but the huge flexibility comes to the cost of an increased difficulty of solution. The complexity of the task is enlarged even more by the fact that real multi-terrain robots move in partially observable environments. Notwithstanding these complications, being able to smoothly control a multi-terrain robot can produce huge benefits in impaired people daily lives or in search and rescue situations.
