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Value Iteration Networks

arXiv 1602.02867 ↗First posted 9 Feb 2016cs.AI cs.LG cs.NEcited by 158

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Abstract (arXiv)

We introduce the value iteration network (VIN): a fully differentiable neural network with a `planning module' embedded within. VINs can learn to plan, and are suitable for predicting outcomes that involve planning-based reasoning, such as policies for reinforcement learning. Key to our approach is a novel differentiable approximation of the value-iteration algorithm, which can be represented as a convolutional neural network, and trained end-to-end using standard backpropagation. We evaluate VIN based policies on discrete and continuous path-planning domains, and on a natural-language based search task. We show that by learning an explicit planning computation, VIN policies generalize better to new, unseen domains.

Key facts. Cited by 158 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Feb 2016.

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Citing papers per year: 2018: 1; 2019: 1; 2020: 2; 2021: 42; 2022: 39; 2023: 29; 2024: 18; 2025: 14; 2026: 12.

158 papers.

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  1. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,873
  2. 2022Planning with Diffusion for Flexible Behavior Synthesiscited by 1,023
  3. 2020MOPO: Model-based Offline Policy Optimizationcited by 630
  4. 2021Combinatorial optimization and reasoning with graph neural networkscited by 267
  5. 2021Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networkscited by 108
  6. 2021A Persistent Spatial Semantic Representation for High-level Natural Language Instruction Executioncited by 91
  7. 2022A Survey on Model-based Reinforcement Learningcited by 65
  8. 2022Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulationcited by 61
  9. 2021Learning Task Informed Abstractionscited by 58
  10. 2021Muesli: Combining Improvements in Policy Optimizationcited by 53
  11. 2022Motion Policy Networkscited by 53
  12. 2024Bridging State and History Representations: Understanding Self-Predictive RLcited by 52
38 more
  1. 2021Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiationcited by 35
  2. 2021Differentiable SLAM-net: Learning Particle SLAM for Visual Navigationcited by 34
  3. 2021Proper Value Equivalencecited by 33
  4. 2023Context-Aware Planning and Environment-Aware Memory for Instruction Following Embodied Agentscited by 33
  5. 2023Adaptive Online Replanning with Diffusion Modelscited by 33
  6. 2021INVIGORATE: Interactive Visual Grounding and Grasping in Cluttercited by 32
  7. 2021Survey on reinforcement learning for language processingcited by 31
  8. 2021Distributed Heuristic Multi-Agent Path Finding with Communicationcited by 31
  9. 2022Chain of Thought Imitation with Procedure Cloningcited by 30
  10. 2022Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objectivecited by 28
  11. 2021Differentiable Spatial Planning using Transformerscited by 25
  12. 2022End-to-End Trajectory Distribution Prediction Based on Occupancy Grid Mapscited by 22
  13. 2021Neural Algorithmic Reasoners are Implicit Plannerscited by 21
  14. 2023Explore to Generalize in Zero-Shot RLcited by 20
  15. 2023Simplified Temporal Consistency Reinforcement Learningcited by 17
  16. 2022Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networkscited by 16
  17. 2021HALMA: Humanlike Abstraction Learning Meets Affordance in Rapid Problem Solvingcited by 15
  18. 2022Navigating to Objects in Unseen Environments by Distance Predictioncited by 15
  19. 2022Value-Consistent Representation Learning for Data-Efficient Reinforcement Learningcited by 15
  20. 2025Horizon Generalization in Reinforcement Learningcited by 15
  21. 2023Query-Policy Misalignment in Preference-Based Reinforcement Learningcited by 14
  22. 2024Learning to Walk from Three Minutes of Real-World Data with Semi-structured Dynamics Modelscited by 14
  23. 2019Off-road Autonomous Vehicles Traversability Analysis and Trajectory Planning Based on Deep Inverse Reinforcement Learningcited by 13
  24. 2021High-Accuracy Model-Based Reinforcement Learning, a Surveycited by 13
  25. 2022Coarse-to-Fine Q-attention with Learned Path Rankingcited by 13
  26. 2022Learning Robotic Navigation from Experience: Principles, Methods, and Recent Resultscited by 13
  27. 2022TransPath: Learning Heuristics For Grid-Based Pathfinding via Transformerscited by 13
  28. 2023Thinker: Learning to Plan and Actcited by 13
  29. 2023Factor Graph Neural Networkscited by 13
  30. 2021Closing the Planning-Learning Loop with Application to Autonomous Drivingcited by 12
  31. 2022Goal-Space Planning with Subgoal Modelscited by 12
  32. 2021Neuro-algorithmic Policies enable Fast Combinatorial Generalizationcited by 11
  33. 2021Hierarchies of Planning and Reinforcement Learning for Robot Navigationcited by 11
  34. 2022Learning on the Job: Self-Rewarding Offline-to-Online Finetuning for Industrial Insertion of Novel Connectors from Visioncited by 11
  35. 2021Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPScited by 10
  36. 2021Motion Planning Transformers: A Motion Planning Framework for Mobile Robotscited by 10
  37. 2022Learning Sampling Distributions for Model Predictive Controlcited by 10
  38. 2022Learning to Optimize in Model Predictive Controlcited by 10

50 most cited of 158 All 158 (JSON)