  This paper presents an approach to exploring a multi-objective reinforcement
learning problem with Model-Agnostic Meta-Learning. The environment we used
consists of a 2D vehicle equipped with a LIDAR sensor. The goal of the
environment is to reach some pre-determined target location but also
effectively avoid any obstacles it may find along its path. We also compare
this approach against a baseline TD3 solution that attempts to solve the same
problem.
