  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. We transferred the designed algorithm to a simple
embedded environment to test the computational load of the algorithm when
running on a mobile robot. Experiments show that when deployed on a robot
platform, the DRL-based algorithm in this article can achieve better planning
results and consume less computing resources.
