Optimising Stochastic Routing for Taxi Fleets with Model Enhanced Reinforcement Learning · Shen RenQianxiao LiLiye ZhangZheng QinBo Yang. Engineering, Computer ...
Nov 1, 2023 · To learn the charging and order-dispatching policy in a dynamic stochastic environment, an online approximation algorithm is developed, which ...
Dec 11, 2023 · Therefore, we used the auto-encoder proximal policy optimization (PPO) model as our base model to generate optimal evacuation routes. To better ...
[PDF] Reinforcement Learning from Optimization Proxy for Ride-Hailing ...
www.ijcai.org › proceedings
It first applies supervised learning to obtain an optimiza- tion proxy for a relocation optimization, i.e., it trains a machine learning model that approximates ...
Missing: Optimising Enhanced
Additionally, the work presented in [57] integrated optimisation, machine learning, and model predictive control to enhance real-time dispatching in ride- ...
Dec 5, 2023 · The first one aims at studying the contribution of transfer learning to the resolution of combinatorial optimization problems using neural ...
Optimising Stochastic Routing for Taxi Fleets with Model Enhanced Reinforcement Learning. The future of mobility-as-a-Service (Maas)should embrace an ...
This paper presents directions for using reinforcement learning with neural networks for dy- namic vehicle routing problems (DVRPs).
Jan 18, 2022 · Online ride-hailing platforms have reduced significantly the amounts of the time that taxis are idle and that passengers spend on waiting.
This paper introduces a reinforcement learning approach to optimize the Stochastic Vehicle Routing Problem with Time Windows (SVRP), focusing on reducing travel ...