  Motion planning is a crucial aspect of robot autonomy as it involves
identifying a feasible motion path to a destination while taking into
consideration various constraints, such as input, safety, and performance
constraints, without violating either system or environment boundaries. This
becomes particularly challenging when multiple robots run without
communication, which compromises their real-time efficiency, safety, and
performance. In this paper, we present a learning-based potential field
algorithm that incorporates deep reinforcement learning into an artificial
potential field (APF). Specifically, we introduce an observation embedding
mechanism that pre-processes dynamic information about the environment and
develop a soft wall-following rule to improve trajectory smoothness. Our
method, while belonging to reactive planning, implicitly encodes environmental
properties. Additionally, our approach can scale up to any number of robots and
has demonstrated superior performance compared to APF and RL through numerical
simulations. Finally, experiments are conducted to highlight the effectiveness
of our proposed method.
