Animals have remarkable abilities to adapt locomotion to different terrains and tasks. However, robots trained by means of reinforcement learning are typically able to solve only a single task and a transferred policy is usually inferior to that trained from scratch. In this work, we demonstrate that meta-reinforcement learning can be used to successfully train a robot capable to solve a wide range of locomotion tasks. The performance of the meta-trained robot is similar to that of a robot that is trained on a single task.

翻译：动物具有适应不同地形和任务移动的非凡能力。 然而,通过强化学习培训的机器人通常只能解决单一任务,而转移的政策通常比从零开始培训的政策低。 在这项工作中,我们证明元强化学习可以用来成功地训练一个能够解决多种移动任务的机器人。经过元培训的机器人的性能类似于受过单一任务培训的机器人。