経験型強化学習における仮想個体から実ロボットへの学習行動の伝播

概要

論文の詳細を見る
For a physical robot to acquire behaviors, it is important for it to learn in the physical environment. Since reinforcement learning requires large computation costs as well as a lot of time in the physical environment, most research has performed learning by simulation. However, this does not work well in the real world. Realizing reinforcement learning of a physical robot in a physical environment requires both an adaptation for the diversity of possible situations and a high-speed learning method that can learn from fewer trials. This paper describes cooperative reinforcement learning based on propagating the learned behaviors of a virtual agent to a physical robot in order to accelerate learning in a physical environment. The method consists of two parts: (1) preparation learning in a virtual environment to accelerate initial learning, which accounts for most of the learning cost ; and, (2) refinement learning in a physical environment by using the virtual learning results as an initial behavior set of a physical robot. Experimental results are given for a ball-pushing task with the physical robot and a virtual agent.
社団法人人工知能学会の論文
1997-07-01