Learn Once Plan Arbitrarily (LOPA): Attention-Enhanced Deep
  Reinforcement Learning Method for Global Path Planning
Guoming Huang Mingxin Hou Xiaofang Yuan Shuqiao Huang Yaonan Wang
Deep reinforcement learning (DRL) methods have recently shown promise in path
planning tasks. However, when dealing with global planning tasks, these methods
face serious challenges such as poor convergence and generalization. To this
end, we propose an attention-enhanced DRL method called LOPA (Learn Once Plan
Arbitrarily) in this paper. Firstly, we analyze the reasons of these problems
from the perspective of DRL's observation, revealing that the traditional
design causes DRL to be interfered by irrelevant map information. Secondly, we
develop the LOPA which utilizes a novel attention-enhanced mechanism to attain
an improved attention capability towards the key information of the
observation. Such a mechanism is realized by two steps: (1) an attention model
is built to transform the DRL's observation into two dynamic views: local and
global, significantly guiding the LOPA to focus on the key information on the
given maps; (2) a dual-channel network is constructed to process these two
views and integrate them to attain an improved reasoning capability. The LOPA
is validated via multi-objective global path planning experiments. The result
suggests the LOPA has improved convergence and generalization performance as
well as great path planning efficiency.