Copertina

Single- vs. Dual-Policy Reinforcement Learning for Dynamic Bike Rebalancing

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Liang, Jiaqi, Liu, Defeng, Jena, Sanjay Dominik, Lodi, Andrea, Vidal, Thibaut
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Optimization and Control
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Dual Policy Reinforcement Learning for Real-time Rebalancing in Bike-sharing Systems
di: Liang, Jiaqi, et al.
Pubblicazione: (2024)

From Sequential Nodes to GPU Batches: Parallel Branch and Bound for Optimal $k$-Sparse GLMs
di: Liu, Jiachang, et al.
Pubblicazione: (2026)

How hard is learning to cut? Trade-offs and sample complexity
di: Khalife, Sammy, et al.
Pubblicazione: (2025)

Learning to Handle Parameter Perturbations in Combinatorial Optimization: an Application to Facility Location
di: Lodi, Andrea, et al.
Pubblicazione: (2019)

Scalable First-order Method for Certifying Optimal k-Sparse GLMs
di: Liu, Jiachang, et al.
Pubblicazione: (2025)

GPU-friendly and Linearly Convergent First-order Methods for Certifying Optimal $k$-sparse GLMs
di: Liu, Jiachang, et al.
Pubblicazione: (2026)

Double Duality: Variational Primal-Dual Policy Optimization for Constrained Reinforcement Learning
di: Li, Zihao, et al.
Pubblicazione: (2024)

Machine Learning Augmented Branch and Bound for Mixed Integer Linear Programming
di: Scavuzzo, Lara, et al.
Pubblicazione: (2024)

Learning-based Online Optimization for Autonomous Mobility-on-Demand Fleet Control
di: Jungel, Kai, et al.
Pubblicazione: (2023)

Don't Explain Noise: Robust Counterfactuals for Randomized Ensembles
di: Forel, Alexandre, et al.
Pubblicazione: (2022)

The impact of machine learning forecasting on strategic decision-making for Bike Sharing Systems
di: Angelelli, Enrico, et al.
Pubblicazione: (2026)

The Differentiable Feasibility Pump
di: Cacciola, Matteo, et al.
Pubblicazione: (2024)

Solving 0-1 Integer Programs with Unknown Knapsack Constraints Using Membership Oracles
di: Messana, Rosario, et al.
Pubblicazione: (2024)

Learning Graph Laplacian with MCP
di: Zhang, Yangjing, et al.
Pubblicazione: (2020)

Free Lunch in the Forest: Functionally-Identical Pruning of Boosted Tree Ensembles
di: Emine, Youssouf, et al.
Pubblicazione: (2024)

DistrictNet: Decision-aware learning for geographical districting
di: Ahmed, Cheikh, et al.
Pubblicazione: (2024)

PACE: Prune-And-Compress Ensemble Models
di: Akkerman, Fabian, et al.
Pubblicazione: (2026)

Fully Dynamic Rebalancing in Dockless Bike-Sharing Systems via Deep Reinforcement Learning
di: Scarpel, Edoardo, et al.
Pubblicazione: (2026)

Batched First-Order Methods for Parallel LP Solving in MIP
di: Blin, Nicolas, et al.
Pubblicazione: (2026)

Solving Max-Cut to Global Optimality via Feasibility-Preserving Graph Neural Networks
di: Chen, Hao, et al.
Pubblicazione: (2026)

SMiLE: Provably Enforcing Global Relational Properties in Neural Networks
di: Francobaldi, Matteo, et al.
Pubblicazione: (2025)

An improved column-generation-based matheuristic for learning classification trees
di: Patel, Krunal Kishor, et al.
Pubblicazione: (2023)

Achieve Performatively Optimal Policy for Performative Reinforcement Learning
di: Chen, Ziyi, et al.
Pubblicazione: (2025)

Contextual Stochastic Vehicle Routing with Time Windows
di: Serrano, Breno, et al.
Pubblicazione: (2024)

A Survey of Contextual Optimization Methods for Decision Making under Uncertainty
di: Sadana, Utsav, et al.
Pubblicazione: (2023)

Finite-Time Analysis of On-Policy Heterogeneous Federated Reinforcement Learning
di: Zhang, Chenyu, et al.
Pubblicazione: (2024)

A Two-Timescale Primal-Dual Framework for Reinforcement Learning via Online Dual Variable Guidance
di: Wolter, Axel Friedrich, et al.
Pubblicazione: (2025)

Sampling-based Safe Reinforcement Learning for Nonlinear Dynamical Systems
di: Suttle, Wesley A., et al.
Pubblicazione: (2024)

Learning Large Neighborhood Search for Maritime Inventory Routing Optimization
di: Chen, Rui, et al.
Pubblicazione: (2025)

Traversing Pareto Optimal Policies: Provably Efficient Multi-Objective Reinforcement Learning
di: Qiu, Shuang, et al.
Pubblicazione: (2024)

Linear-Quadratic Mean-Field Reinforcement Learning: Convergence of Policy Gradient Methods
di: Carmona, René, et al.
Pubblicazione: (2019)

TreeDQN: Sample-Efficient Off-Policy Reinforcement Learning for Combinatorial Optimization
di: Sorokin, D., et al.
Pubblicazione: (2023)

Dynamic Facility Location under Cumulative Customer Demand
di: Silva, Warley Almeida, et al.
Pubblicazione: (2024)

Stackelberg Dynamic Location Planning under Cumulative Demand
di: Silva, Warley Almeida, et al.
Pubblicazione: (2026)

Convergence and stability of Q-learning in Hierarchical Reinforcement Learning
di: Manenti, Massimiliano, et al.
Pubblicazione: (2025)

Sample Efficient Reinforcement Learning with Partial Dynamics Knowledge
di: Alharbi, Meshal, et al.
Pubblicazione: (2023)

Joint MDPs and Reinforcement Learning in Coupled-Dynamics Environments
di: Kaya, Ege C., et al.
Pubblicazione: (2026)

Natural Policy Gradient and Actor Critic Methods for Constrained Multi-Task Reinforcement Learning
di: Zeng, Sihan, et al.
Pubblicazione: (2024)

Policy Transfer for Continuous-Time Reinforcement Learning: A (Rough) Differential Equation Approach
di: Guo, Xin, et al.
Pubblicazione: (2025)

Deep Reinforcement Learning for Dynamic Order Picking in Warehouse Operations
di: Mahmoudinazlou, Sasan, et al.
Pubblicazione: (2024)