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

Collision Avoidance Robotics Via Meta-Learning (CARML)