On the public citation graph

A Sequence Aware Feasibility Constrained Hierarchical Reinforcement Learning Framework for Electric Vehicle Charging Navigation under Dynamic Uncertainty

0 signed · 24 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1016/j.apenergy.2026.127355openalex-snapshot
Heterogeneous attention-driven deep reinforcement learning for solving EVRPs with pickup-delivery and time windows
inferredcites →https://doi.org/10.1016/j.artint.2025.104382openalex-snapshot
Reinforcement learning in convergently non-stationary environments: Feudal hierarchies and learned representations
inferredcites →https://doi.org/10.1016/j.ejor.2021.06.021openalex-snapshot
Deep Q-learning for same-day delivery with vehicles and drones
inferredcites →https://doi.org/10.1016/j.est.2022.105013openalex-snapshot
A study on trends and developments in electric vehicle charging technologies
inferredcites →https://doi.org/10.1016/j.eswa.2025.127331openalex-snapshot
Explainable and context-aware Graph Neural Networks for dynamic electric vehicle route optimization to optimal charging station
inferredcites →https://doi.org/10.1016/j.ijepes.2024.109823openalex-snapshot
Electric vehicle charging navigation strategy in coupled smart grid and transportation network: A hierarchical reinforcement learning approach
inferredcites →https://doi.org/10.1016/j.trb.2017.02.004openalex-snapshot
The electric vehicle routing problem with nonlinear charging function
inferredcites →https://doi.org/10.1016/j.trd.2017.08.022openalex-snapshot
Optimising route choices for the travelling and charging of battery electric vehicles by considering multiple objectives
inferredcites →https://doi.org/10.1016/j.trd.2018.04.002openalex-snapshot
A review of consumer preferences of and interactions with electric vehicle charging infrastructure
inferredcites →https://doi.org/10.1016/j.tre.2021.102496openalex-snapshot
Dynamic stochastic electric vehicle routing with safe reinforcement learning
inferredcites →https://doi.org/10.1038/s41598-025-24611-6openalex-snapshot
Correction: Optimizing electric vehicle energy consumption prediction through machine learning and ensemble approaches
inferredcites →https://doi.org/10.1109/jiot.2022.3181613openalex-snapshot
Shortest-Path-Based Deep Reinforcement Learning for EV Charging Routing Under Stochastic Traffic Condition and Electricity Prices
inferredcites →https://doi.org/10.1109/jiot.2023.3244854openalex-snapshot
Smart Navigation and Energy Management Framework for Autonomous Electric Vehicles in Complex Environments
inferredcites →https://doi.org/10.1109/tase.2025.3632767openalex-snapshot
TERRAN: A Transformer-Based Electric Vehicle Routing Agent for Real-Time Adaptive Navigation
inferredcites →https://doi.org/10.1109/tase.2025.3632970openalex-snapshot
Optimizing Trip Planning of Electric Vehicles Using Deep Reinforcement Learning
inferredcites →https://doi.org/10.1109/tii.2022.3210264openalex-snapshot
A Graph Reinforcement Learning-Based Decision-Making Platform for Real-Time Charging Navigation of Urban Electric Vehicles
inferredcites →https://doi.org/10.1109/tits.2023.3334976openalex-snapshot
Machine Learning to Solve Vehicle Routing Problems: A Survey
inferredcites →https://doi.org/10.1109/tnnls.2022.3207346openalex-snapshot
Deep Reinforcement Learning: A Survey
inferredcites →https://doi.org/10.1109/tsg.2019.2942593openalex-snapshot
Deep Reinforcement Learning for EV Charging Navigation by Coordinating Smart Grid and Intelligent Transportation System
inferredcites →https://doi.org/10.1109/tsg.2022.3214680openalex-snapshot
A Constraint-Based Routing and Charging Methodology for Battery Electric Vehicles With Deep Reinforcement Learning
inferredcites →https://doi.org/10.1109/tte.2025.3574546openalex-snapshot
Attention-Enhanced Deep Reinforcement Learning for Electric Vehicle Routing Optimization
inferredcites →https://doi.org/10.26599/jicv.2023.9210020openalex-snapshot
Future Role of Artificial Intelligence in Advancing Transportation Electrification
inferredcites →https://doi.org/10.3390/drones8020060openalex-snapshot
Dynamic Scene Path Planning of UAVs Based on Deep Reinforcement Learning
inferredcites →https://doi.org/10.48130/dts-0024-0020openalex-snapshot
Systematic review of the impacts of electric vehicles on evolving transportation systems
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.