论文标题
通过元学习（CARML）避免碰撞机器人技术
Collision Avoidance Robotics Via Meta-Learning (CARML)
论文作者
论文摘要
本文提出了一种通过模型不合命中的学习方法来探索多目标增强学习问题的方法。我们使用的环境由配备LIDAR传感器的2D车组成。环境的目的是达到一些预定的目标位置，但也有效地避免了沿其路径可能发现的任何障碍。我们还将这种方法与试图解决相同问题的基线TD3解决方案进行了比较。
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.
