Application of direct-vision-based reinforcement learning to a real mobile robot
Application of direct-vision-based reinforcement learning to a real mobile robot
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基于直视的强化学习在真实移动机器人中的应用
DOI：
10.1109/iconip.2002.1201956
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发表时间：
2002
期刊：
影响因子：
--
通讯作者：
K. Shibata
中科院分区：
文献类型：
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作者：
Masaru Iida;Masanori Sugisaka;K. Shibata
本文通过基于直接视觉的强化学习(RL)，证实了具有简单视觉传感器的真实移动机器人能够学习适当的动作以到达目标。在基于Direct-Vision的RL中，将原始视觉感觉信号直接输入到一个分层的神经网络中，并利用基于强化学习产生的训练信号对神经网络进行反向传播训练。考虑到获得视觉感觉信号的时间延迟，提出了使用提前两个时间步长的评论者输出来训练演员输出。研究表明，具有单色视觉传感器的机器人无需任何先验知识和人类的帮助，通过从头开始学习，即可获得到达目标物体的动作。
In this paper, it was confirmed that a real mobile robot with a simple visual sensor could learn appropriate actions to reach a target by Direct-Vision-Based reinforcement learning (RL). In Direct-Vision-Based RL, raw visual sensory signals are put into a layered neural network directly, an the neural network is trained by Back Propagation using the training signal that is generated based on reinforcement learning. Considering the time delay to get the visual sensory signals, it was proposed that the actor outputs are trained using the critic output at two time steps ahead. It was shown that the robot with a monochrome visual sensor could obtain reaching actions to a target object through the learning from scratch without any advance knowledge and any help of humans.