Motivated by the challenge of achieving rapid learning in physical
environments, this paper presents the development and training of a robotic
system designed to navigate and solve a labyrinth game using model-based
reinforcement learning techniques. The method involves extracting
low-dimensional observations from camera images, along with a cropped and
rectified image patch centered on the current position within the labyrinth,
providing valuable information about the labyrinth layout. The learning of a
control policy is performed purely on the physical system using model-based
reinforcement learning, where the progress along the labyrinth's path serves as
a reward signal. Additionally, we exploit the system's inherent symmetries to
augment the training data. Consequently, our approach learns to successfully
solve a popular real-world labyrinth game in record time, with only 5 hours of
real-world training data.

通过基于模型的强化学习技术，本研究提出了一种用于导航和解决迷宫游戏的机器人系统的开发与训练。该方法通过从摄像头图像中提取低维观测以及裁剪和校正的以迷宫当前位置为中心的图像块，为迷宫布局提供了有价值的信息。控制策略的学习完全在物理系统上进行，使用基于模型的强化学习方法，其中沿着迷宫路径的进展作为奖励信号。此外，我们利用系统固有的对称性增加了训练数据。因此，我们的方法通过仅使用 5 小时的真实世界训练数据，在极短时间内成功解决了一种流行的真实世界迷宫游戏。