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Route planning is essential to mobile robot navigation problems. In recentyears, deep reinforcement learning (DRL) has been applied to learning optimalplanning 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 inthe environment is high. In this work, we propose a distributionalreinforcement learning based framework that learns return distributions whichexplicitly reflect environmental stochasticity. Policies based on thesecond-order stochastic dominance (SSD) relation can be used to make adjustableroute decisions according to user preference on performance robustness. Ourproposed method is evaluated in a simulated road network environment, andexperimental results show that our method is able to plan the shortest routesthat minimize stochasticity in travel time when robustness is preferred, whileother state-of-the-art DRL methods are agnostic to environmental stochasticity.

Robust Route Planning with Distributional Reinforcement Learning in a Stochastic Road Network Environment