  For real-world deployments, it is critical to allow robots to navigate in
complex environments autonomously. Traditional methods usually maintain an
internal map of the environment, and then design several simple rules, in
conjunction with a localization and planning approach, to navigate through the
internal map. These approaches often involve a variety of assumptions and prior
knowledge. In contrast, recent reinforcement learning (RL) methods can provide
a model-free, self-learning mechanism as the robot interacts with an initially
unknown environment, but are expensive to deploy in real-world scenarios due to
inefficient exploration. In this paper, we focus on efficient navigation with
the RL technique and combine the advantages of these two kinds of methods into
a rule-based RL (RuRL) algorithm for reducing the sample complexity and cost of
time. First, we use the rule of wall-following to generate a closed-loop
trajectory. Second, we employ a reduction rule to shrink the trajectory, which
in turn effectively reduces the redundant exploration space. Besides, we give
the detailed theoretical guarantee that the optimal navigation path is still in
the reduced space. Third, in the reduced space, we utilize the Pledge rule to
guide the exploration strategy for accelerating the RL process at the early
stage. Experiments conducted on real robot navigation problems in hex-grid
environments demonstrate that RuRL can achieve improved navigation performance.
