確率的道路網環境における分布強化学習によるロバスト経路計画
Robust Route Planning with Distributional Reinforcement Learning in a Stochastic Road Network Environment
環境の確率的変動を考慮したリターン分布を学習し、ユーザのロバスト性選好に応じて経路を調整できる分布強化学習フレームワークを提案した。
著者: Xi Lin, Paul Szenher, John D. Martin, Brendan Englot
分類: cs.RO
原文アブストラクト
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.
