3 citations · 4 across the 7 of their papers we have counts for
7 papers
A Pontryagin Method of Model-based Reinforcement Learning via Hamiltonian Actor-Critic
Chengyang Gu, Yuxin Pan, Hui Xiong +1
Model-based reinforcement learning (MBRL) improves sample efficiency by leveraging learned dynamics models for policy optimization. However, the effectiveness of methods such as ac…
Multi-Task Vehicle Routing Solver via Mixture of Specialized Experts under State-Decomposable MDP
Yuxin Pan, Zhiguang Cao, Chengyang Gu +4
Existing neural methods for multi-task vehicle routing problems (VRPs) typically learn unified solvers to handle multiple constraints simultaneously. However, they often underutili…
BEAVER: Building Environments with Assessable Variation for Evaluating Multi-Objective Reinforcement Learning
Ruohong Liu, Jack Umenberger, Yize Chen
Recent years have seen significant advancements in designing reinforcement learning (RL)-based agents for building energy management. While individual success is observed in simula…
Hierarchical Learning-based Graph Partition for Large-scale Vehicle Routing Problems
Yuxin Pan, Ruohong Liu, Yize Chen +2
Neural solvers based on the divide-and-conquer approach for Vehicle Routing Problems (VRPs) in general, and capacitated VRP (CVRP) in particular, integrates the global partition of…
C-MORL: Multi-Objective Reinforcement Learning through Efficient Discovery of Pareto Front
Ruohong Liu, Yuxin Pan, Linjie Xu +4
Multi-objective reinforcement learning (MORL) excels at handling rapidly changing preferences in tasks that involve multiple criteria, even for unseen preferences. However, previou…
Learning and Optimization for Price-based Demand Response of Electric Vehicle Charging
Chengyang Gu, Yuxin Pan, Ruohong Liu +1
In the context of charging electric vehicles (EVs), the price-based demand response (PBDR) is becoming increasingly significant for charging load management. Such response usually…
Adjustable Robust Reinforcement Learning for Online 3D Bin Packing
Yuxin Pan, Yize Chen, Fangzhen Lin
Designing effective policies for the online 3D bin packing problem (3D-BPP) has been a long-standing challenge, primarily due to the unpredictable nature of incoming box sequences…