  In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge,
participants were tasked with building a controller for a musculoskeletal model
with a goal of matching a given time-varying velocity vector. Top participants
were invited to describe their algorithms. In this work, we describe the
challenge and present thirteen solutions that used deep reinforcement learning
approaches. Many solutions use similar relaxations and heuristics, such as
reward shaping, frame skipping, discretization of the action space, symmetry,
and policy blending. However, each team implemented different modifications of
the known algorithms by, for example, dividing the task into subtasks, learning
low-level control, or by incorporating expert knowledge and using imitation
learning.

Machine Learning;Robotics;Machine Learning