  Route planning is essential to mobile robot navigation problems. In recent
years, deep reinforcement learning (DRL) has been applied to learning optimal
planning policies in stochastic environments without prior knowledge. However,
existing works focus on learning policies that maximize the expected return,
the performance of which can vary greatly when the level of stochasticity in
the environment is high. In this work, we propose a distributional
reinforcement learning based framework that learns return distributions which
explicitly reflect environmental stochasticity. Policies based on the
second-order stochastic dominance (SSD) relation can be used to make adjustable
route decisions according to user preference on performance robustness. Our
proposed method is evaluated in a simulated road network environment, and
experimental results show that our method is able to plan the shortest routes
that minimize stochasticity in travel time when robustness is preferred, while
other state-of-the-art DRL methods are agnostic to environmental stochasticity.
