mlsanityData to 14 Oct 2026

A Survey on Applications of Reinforcement Learning in Spatial Resource Allocation

arXiv 2403.03643 ↗First posted 6 Mar 2024cs.LG cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

The challenge of spatial resource allocation is pervasive across various domains such as transportation, industry, and daily life. As the scale of real-world issues continues to expand and demands for real-time solutions increase, traditional algorithms face significant computational pressures, struggling to achieve optimal efficiency and real-time capabilities. In recent years, with the escalating computational power of computers, the remarkable achievements of reinforcement learning in domains like Go and robotics have demonstrated its robust learning and sequential decision-making capabilities. Given these advancements, there has been a surge in novel methods employing reinforcement learning to tackle spatial resource allocation problems. These methods exhibit advantages such as rapid solution convergence and strong model generalization abilities, offering a new perspective on resolving spatial resource allocation problems. Therefore, this paper aims to summarize and review recent theoretical methods and applied research utilizing reinforcement learning to address spatial resource allocation problems. It provides a summary and comprehensive overview of its fundamental principles, related methodologies, and applied research. Additionally, it highlights several unresolved issues that urgently require attention in this direction for the future.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 54 of its 227 references that are in the map. First posted on arXiv 6 Mar 2024.

Builds on

54 of 227 references.

References · 54

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  3. 2015Continuous control with deep reinforcement learningcited by 3,407
  4. 2015Trust Region Policy Optimizationcited by 2,580
  5. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
  6. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,920
  7. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  8. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  9. 2019Mastering Atari, Go, Chess and Shogi by Planning with a Learned Modelcited by 1,353
  10. 2015Prioritized Experience Replaycited by 1,099
  11. 2017Rainbow: Combining Improvements in Deep Reinforcement Learningcited by 786
  12. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 785
38 more
  1. 2020Deep Reinforcement Learning for Autonomous Driving: A Surveycited by 721
  2. 2019Model-Based Reinforcement Learning for Ataricited by 502
  3. 2017A Brief Survey of Deep Reinforcement Learningcited by 483
  4. 2017Distributional Reinforcement Learning with Quantile Regressioncited by 474
  5. 2021Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learningcited by 371
  6. 2018Implicit Quantile Networks for Distributional Reinforcement Learningcited by 369
  7. 2017Deep Reinforcement Learning: An Overviewcited by 333
  8. 2016Safe, Multi-Agent, Reinforcement Learning for Autonomous Drivingcited by 311
  9. 2018Residual Reinforcement Learning for Robot Controlcited by 274
  10. 2017Noisy Networks for Explorationcited by 266
  11. 2018Human-level performance in first-person multiplayer games with population-based deep reinforcement learningcited by 264
  12. 2017Deep Reinforcement Learning framework for Autonomous Drivingcited by 225
  13. 2019A Theoretical Analysis of Deep Q-Learningcited by 215
  14. 2018Mean Field Multi-Agent Reinforcement Learningcited by 207
  15. 2021Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Surveycited by 130
  16. 2019Survey on Evaluation Methods for Dialogue Systemscited by 126
  17. 2019Model-free Deep Reinforcement Learning for Urban Autonomous Drivingcited by 98
  18. 2016Playing FPS Games with Deep Reinforcement Learningcited by 95
  19. 2021Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directionscited by 73
  20. 2020Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorialcited by 60
  21. 2019Efficient Ridesharing Order Dispatching with Mean Field Multi-Agent Reinforcement Learningcited by 57
  22. 2019DeepPool: Distributed Model-free Algorithm for Ride-sharing using Deep Reinforcement Learningcited by 54
  23. 2020Self-Supervised Reinforcement Learning for Recommender Systemscited by 49
  24. 2021A Deep Value-network Based Approach for Multi-Driver Order Dispatchingcited by 45
  25. 2019Real-Time Reinforcement Learningcited by 40
  26. 2019Neural Approximate Dynamic Programming for On-Demand Ride-Poolingcited by 33
  27. 2017Trust-PCL: An Off-Policy Trust Region Method for Continuous Controlcited by 30
  28. 2019Deep Reinforcement Learning for Multi-Driver Vehicle Dispatching and Repositioning Problemcited by 29
  29. 2019Multi-Agent Reinforcement Learning for Order-dispatching via Order-Vehicle Distribution Matchingcited by 26
  30. 2019CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platformscited by 22
  31. 2018Cooperative and Distributed Reinforcement Learning of Drones for Field Coveragecited by 20
  32. 2020A Distributed Model-Free Ride-Sharing Approach for Joint Matching, Pricing, and Dispatching using Deep Reinforcement Learningcited by 17
  33. 2019A Distributed Model-Free Algorithm for Multi-hop Ride-sharing using Deep Reinforcement Learningcited by 11
  34. 2019Can Sophisticated Dispatching Strategy Acquired by Reinforcement Learning? - A Case Study in Dynamic Courier Dispatching Systemcited by 8
  35. 2016Dynamic Frame skip Deep Q Networkcited by 5
  36. 2021Multi-Agent Reinforcement Learning of 3D Furniture Layout Simulation in Indoor Graphics Scenescited by 5
  37. 2021A Deep Reinforcement Learning Approach for the Meal Delivery Problemcited by 5
  38. 2019Proximal Policy Optimization with Mixed Distributed Trainingcited by 4

50 most cited of 54 All 54 (JSON)

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025A Game-Theoretic Spatio-Temporal Reinforcement Learning Framework for Collaborative Public Resource Allocation
  2. 2025Multi-Agent Reinforcement Learning for Resources Allocation Optimization: A Survey
  3. 2026Reinforcement Learning in Operational Research: A Technical Review and Practical Roadmap
  4. 2025Situational-Constrained Sequential Resources Allocation via Reinforcement Learning
  5. 2025Optimizing Job Allocation using Reinforcement Learning with Graph Neural Networks
  6. 2025Path Planning through Multi-Agent Reinforcement Learning in Dynamic Environments
  7. 2024Large-scale Urban Facility Location Selection with Knowledge-informed Reinforcement Learning
  8. 2026Reinforcement Learning Methods for Neighborhood Selection in Local Search