1 citations · 1 across the 4 of their papers we have counts for
7 papers
SAVGO: Learning State-Action Value Geometry with Cosine Similarity for Continuous Control
Stavros Orfanoudakis, Pedro P. Vergara
While representation and similarity learning have improved the sample efficiency of Reinforcement Learning (RL), they are rarely used to shape policy updates directly in the action…
Learning to Route Electric Trucks Under Operational Uncertainty
Stavros Orfanoudakis, Ziyan Li, Ruixiao Yang +4
Electric truck operations require routing decisions that remain feasible under limited battery range, long charging times, travel and energy consumption, and competition for shared…
Topology-Aware Graph Reinforcement Learning for Energy Storage Systems Optimal Dispatch in Distribution Networks
Shuyi Gao, Stavros Orfanoudakis, Shengren Hou +2
Optimal dispatch of energy storage systems (ESSs) in distribution networks involves jointly improving operating economy and voltage security under time-varying conditions and possi…
Physics-Informed Reinforcement Learning for Large-Scale EV Smart Charging Considering Distribution Network Voltage Constraints
Stavros Orfanoudakis, Frans A. Oliehoek, Peter Palensky +1
Electric Vehicles (EVs) offer substantial flexibility for grid services, yet large-scale, uncoordinated charging can threaten voltage stability in distribution networks. Existing R…
Optimizing Electric Vehicles Charging using Large Language Models and Graph Neural Networks
Stavros Orfanoudakis, Peter Palensky, Pedro P. Vergara
Maintaining grid stability amid widespread electric vehicle (EV) adoption is vital for sustainable transportation. Traditional optimization methods and Reinforcement Learning (RL)…
GNN-DT: Graph Neural Network Enhanced Decision Transformer for Efficient Optimization in Dynamic Environments
Stavros Orfanoudakis, Nanda Kishor Panda, Peter Palensky +1
Reinforcement Learning (RL) methods used for solving real-world optimization problems often involve dynamic state-action spaces, larger scale, and sparse rewards, leading to signif…