  Model-free continuous control for robot navigation tasks using Deep
Reinforcement Learning (DRL) that relies on noisy policies for exploration is
sensitive to the density of rewards. In practice, robots are usually deployed
in cluttered environments, containing many obstacles and narrow passageways.
Designing dense effective rewards is challenging, resulting in exploration
issues during training. Such a problem becomes even more serious when tasks are
described using temporal logic specifications. This work presents a deep policy
gradient algorithm for controlling a robot with unknown dynamics operating in a
cluttered environment when the task is specified as a Linear Temporal Logic
(LTL) formula. To overcome the environmental challenge of exploration during
training, we propose a novel path planning-guided reward scheme by integrating
sampling-based methods to effectively complete goal-reaching missions. To
facilitate LTL satisfaction, our approach decomposes the LTL mission into
sub-goal-reaching tasks that are solved in a distributed manner. Our framework
is shown to significantly improve performance (effectiveness, efficiency) and
exploration of robots tasked with complex missions in large-scale cluttered
environments. A video demonstration can be found on YouTube Channel:
https://youtu.be/yMh_NUNWxho.
