  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.
