Deep Reinforcement Learning for Mobile Robot Path Planning

Understand

Path planning is an important problem with the the applications in many aspects, such as video games, robotics etc.

• This paper proposes a novel method to address the problem of Deep Reinforcement Learning (DRL) based path planning for a mobile robot.
• We design DRL-based algorithms, including reward functions, and parameter optimization, to avoid time-consuming work in a 2D environment.
• We also designed an Two-way search hybrid A* algorithm to improve the quality of local path planning.