mlsanityData to 14 Oct 2026

Laplacian Representations for Decision-Time Planning

arXiv 2602.05031 ↗First posted 4 Feb 2026cs.LGcited by 5

On the map: Deep reinforcement learning methods → Deep RL algorithms and representations

Abstract (arXiv)

Planning with a learned model remains a key challenge in model-based reinforcement learning (RL). In decision-time planning, state representations are critical as they must support local cost computation while preserving long-horizon structure. In this paper, we show that the Laplacian representation provides an effective latent space for planning by capturing state-space distances at multiple time scales. This representation preserves meaningful distances and naturally decomposes long-horizon problems into subgoals, also mitigating the compounding errors that arise over long prediction horizons. Building on these properties, we introduce ALPS, a hierarchical planning algorithm, and demonstrate that it outperforms commonly used baselines on a selection of offline goal-conditioned RL tasks from OGBench, a benchmark previously dominated by model-free methods.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods. Builds on 50 of its 87 references that are in the map. First posted on arXiv 4 Feb 2026.

Builds on

50 of 87 references.

Closest ancestors · 4

Same direction, newest first

  1. 2025TD-JEPA: Latent-predictive Representations for Zero-Shot Reinforcement Learning1 Oct
  2. 2025Discovering Temporal Structure: An Overview of Hierarchical Reinforcement Learning16 Jun
  3. 2024Learning Temporal Distances: Contrastive Successor Features Can Provide a Metric Structure for Decision-Making24 Jun
  4. 2024Foundation Policies with Hilbert Representations23 Feb

Other references · 46

  1. 2016Layer Normalizationcited by 5,669
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  3. 2019The Curious Case of Neural Text Degenerationcited by 2,916
  4. 2020D4RL: Datasets for Deep Data-Driven Reinforcement Learningcited by 1,427
  5. 2021Offline Reinforcement Learning with Implicit Q-Learningcited by 1,293
  6. 2018Learning Latent Dynamics for Planning from Pixelscited by 1,264
  7. 2017Hindsight Experience Replaycited by 1,136
  8. 2018Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Modelscited by 774
  9. 2019When to Trust Your Model: Model-Based Policy Optimizationcited by 773
  10. 2023TD-MPC2: Scalable, Robust World Models for Continuous Controlcited by 512
  11. 2018Data-Efficient Hierarchical Reinforcement Learningcited by 455
  12. 2019Learning Latent Plans from Playcited by 316
34 more
  1. 2016Deep Visual Foresight for Planning Robot Motioncited by 285
  2. 2022Contrastive Learning as Goal-Conditioned Reinforcement Learningcited by 253
  3. 2018Visual Reinforcement Learning with Imagined Goalscited by 251
  4. 2024OGBench: Benchmarking Offline Goal-Conditioned RLcited by 238
  5. 2020Accelerating Reinforcement Learning with Learned Skill Priorscited by 223
  6. 2019Benchmarking Model-Based Reinforcement Learningcited by 162
  7. 2022Goal-Conditioned Reinforcement Learning: Problems and Solutionscited by 158
  8. 2016Continuous Deep Q-Learning with Model-based Accelerationcited by 157
  9. 2018Model-Based Value Estimation for Efficient Model-Free Reinforcement Learningcited by 156
  10. 2019Planning with Goal-Conditioned Policiescited by 149
  11. 2023HIQL: Offline Goal-Conditioned RL with Latent States as Actionscited by 141
  12. 2021Goal-Conditioned Reinforcement Learning with Imagined Subgoalscited by 134
  13. 2023Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learningcited by 121
  14. 2017A Laplacian Framework for Option Discovery in Reinforcement Learningcited by 110
  15. 2018Model-Based Reinforcement Learning via Meta-Policy Optimizationcited by 101
  16. 2020Sample-efficient Cross-Entropy Method for Real-time Planningcited by 95
  17. 2019Exploring Model-based Planning with Policy Networkscited by 90
  18. 2024Is Value Learning Really the Main Bottleneck in Offline RL?cited by 89
  19. 2018The Laplacian in RL: Learning Representations with Efficient Approximationscited by 86
  20. 2019Hierarchical Foresight: Self-Supervised Learning of Long-Horizon Tasks via Visual Subgoal Generationcited by 83
  21. 2021Temporal Abstraction in Reinforcement Learning with the Successor Representationcited by 54
  22. 2022Investigating Compounding Prediction Errors in Learned Dynamics Modelscited by 47
  23. 2016Self-Correcting Models for Model-Based Reinforcement Learningcited by 43
  24. 2020Cautious Adaptation For Reinforcement Learning in Safety-Critical Settingscited by 42
  25. 2020Model-Predictive Control via Cross-Entropy and Gradient-Based Optimizationcited by 32
  26. 2023Deep Laplacian-based Options for Temporally-Extended Explorationcited by 29
  27. 2018Hierarchical Reinforcement Learning with Hindsightcited by 26
  28. 2021Towards Better Laplacian Representation in Reinforcement Learning with Generalized Graph Drawingcited by 23
  29. 2023Stabilizing Contrastive RL: Techniques for Robotic Goal Reaching from Offline Datacited by 23
  30. 2018Finding Options that Minimize Planning Timecited by 17
  31. 2023Proper Laplacian Representation Learningcited by 11
  32. 2022Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methodscited by 10
  33. 2022Reachability-Aware Laplacian Representation in Reinforcement Learningcited by 10
  34. 2023PcLast: Discovering Plannable Continuous Latent Statescited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 5.

5 papers.

  1. 2026Spectral Alignment in Forward-Backward Representations via Temporal Abstractioncited by 0
  2. 2026SUN: Reaching for Novelty in Reinforcement Learningcited by 0
  3. 2026Learning Multiple Timescales for Goal-Conditioned Reinforcement Learningcited by 0
  4. 2026Learning Commute-Time-Preserving World Models for Planningcited by 0
  5. 2026Mastering Atari 2600 Games with Discovered Optionscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2022Hierarchical Planning Through Goal-Conditioned Offline Reinforcement Learning
  2. 2024A New View on Planning in Online Reinforcement Learning
  3. 2026Hitting Time Isomorphism for Multi-Stage Planning with Foundation Policies
  4. 2026Learning Multi-Timescale Abstractions for Hierarchical Combinatorial Planning
  5. 2020World Model as a Graph: Learning Latent Landmarks for Planning
  6. 2025Test-Time Graph Search for Goal-Conditioned Reinforcement Learning
  7. 2026Diffusion Subgoal Planning for Long-Horizon Offline Goal-Conditioned Reinforcement Learning
  8. 2021Temporal Predictive Coding For Model-Based Planning In Latent Space