Laplacian Representations for Decision-Time Planning

• URL: http://arxiv.org/abs/2602.05031v1
• Date: Wed, 04 Feb 2026 20:34:50 GMT
• Title: Laplacian Representations for Decision-Time Planning
• Authors: Dikshant Shehmar, Matthew Schlegel, Matthew E. Taylor, Marlos C. Machado,
• Abstract summary: We show that the Laplacian representation provides an effective latent space for planning by capturing state-space distances at multiple time scales.<n>This representation preserves meaningful distances and naturally decomposes long-horizon problems into subgoals, also mitigating the compounding errors that arise over long prediction horizons.<n>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.
• Score: 20.25004555858261
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• SokoBench: Evaluating Long-Horizon Planning and Reasoning in Large Language Models [6.718614728570862]
  We provide a systematic assessment of the planning and long-horizon reasoning capabilities of large language models.<n>We propose a novel benchmark based on Sokoban puzzles, intentionally simplified to isolate long-horizon planning from state persistence.<n>Our findings reveal a consistent degradation in planning performance when more than 25 moves are required to reach the solution.
  arXiv  Detail & Related papers  (2026-01-28T18:56:00Z)
• Optimal Look-back Horizon for Time Series Forecasting in Federated Learning [26.070107882914844]
  This paper presents a principled framework for adaptive horizon selection in federated time series forecasting.<n>We derive a decomposition of the forecasting loss into a Bayesian term, which reflects irreducible uncertainty.<n>We prove that the total forecasting loss is minimized at the smallest horizon where the irreducible loss starts to saturate, while the approximation loss continues to rise.
  arXiv  Detail & Related papers  (2025-11-16T21:46:54Z)
• UnLoc: Leveraging Depth Uncertainties for Floorplan Localization [80.55849461031879]
  UnLoc is an efficient data-driven solution for sequential camera localization within floorplans.<n>We introduce a novel probabilistic model that incorporates uncertainty estimation, modeling depth predictions as explicit probability distributions.<n>We evaluate UnLoc on large-scale synthetic and real-world datasets, demonstrating significant improvements in terms of accuracy and robustness.
  arXiv  Detail & Related papers  (2025-09-14T14:45:43Z)
• State-Covering Trajectory Stitching for Diffusion Planners [29.89423911968709]
  State-Covering Trajectory Stitching (SCoTS) is a reward-free trajectory augmentation method that stitches together short trajectory segments.<n>We demonstrate that SCoTS significantly improves the performance and generalization capabilities of diffusion planners on offline goal-conditioned benchmarks.
  arXiv  Detail & Related papers  (2025-06-01T08:32:22Z)
• Planning Transformer: Long-Horizon Offline Reinforcement Learning with Planning Tokens [1.8416014644193066]
  We introduce Planning Tokens, which contain high-level, long time-scale information about the agent's future. We demonstrate that Planning Tokens improve the interpretability of the model's policy through the interpretable plan visualisations and attention map.
  arXiv  Detail & Related papers  (2024-09-14T19:30:53Z)
• Compositional Foundation Models for Hierarchical Planning [52.18904315515153]
  We propose a foundation model which leverages expert foundation model trained on language, vision and action data individually together to solve long-horizon tasks. We use a large language model to construct symbolic plans that are grounded in the environment through a large video diffusion model. Generated video plans are then grounded to visual-motor control, through an inverse dynamics model that infers actions from generated videos.
  arXiv  Detail & Related papers  (2023-09-15T17:44:05Z)
• Visual Learning-based Planning for Continuous High-Dimensional POMDPs [81.16442127503517]
  Visual Tree Search (VTS) is a learning and planning procedure that combines generative models learned offline with online model-based POMDP planning. VTS bridges offline model training and online planning by utilizing a set of deep generative observation models to predict and evaluate the likelihood of image observations in a Monte Carlo tree search planner. We show that VTS is robust to different observation noises and, since it utilizes online, model-based planning, can adapt to different reward structures without the need to re-train.
  arXiv  Detail & Related papers  (2021-12-17T11:53:31Z)
• Model-Based Reinforcement Learning via Latent-Space Collocation [110.04005442935828]
  We argue that it is easier to solve long-horizon tasks by planning sequences of states rather than just actions. We adapt the idea of collocation, which has shown good results on long-horizon tasks in optimal control literature, to the image-based setting by utilizing learned latent state space models.
  arXiv  Detail & Related papers  (2021-06-24T17:59:18Z)
• Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors [124.30562402952319]
  The ability to predict and plan into the future is fundamental for agents acting in the world. Current learning approaches for visual prediction and planning fail on long-horizon tasks. We propose a framework for visual prediction and planning that is able to overcome both of these limitations.
  arXiv  Detail & Related papers  (2020-06-23T17:58:56Z)
• Supporting Optimal Phase Space Reconstructions Using Neural Network Architecture for Time Series Modeling [68.8204255655161]
  We propose an artificial neural network with a mechanism to implicitly learn the phase spaces properties. Our approach is either as competitive as or better than most state-of-the-art strategies.
  arXiv  Detail & Related papers  (2020-06-19T21:04:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.