aiqbench analysis
This research introduces ALPS, a hierarchical planning algorithm that leverages Laplacian representations to capture multi-scale state distances and mitigate compounding errors in long-horizon reinforcement learning. By effectively decomposing complex tasks into subgoals, the method outperforms standard baselines on offline goal-conditioned RL benchmarks previously dominated by model-free approaches. The work addresses a critical bottleneck in model-based RL, offering a robust latent space design for decision-time planning.
Laplacian Representations for Decision-Time Planning
arXiv:2602.05031v2 Announce Type: replace 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.
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.
Machine Learning (cs.LG)
arXiv:2602.05031 [cs.LG]
(or arXiv:2602.05031v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2602.05031 Focus to learn more arXiv-issued DOI via DataCite
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