Summary
This post introduces Graph Sparse Sampling (GSS), an online planning algorithm by Idan Lev-Yehudi and Vadim Indelman (arXiv 2607.05359) that addresses the computational burden of planning under uncertainty in continuous domains. Tree-search methods such as MCTS require sampling budgets that grow exponentially with lookahead depth due to their branching structure. GSS instead shares sampled futures across multiple candidate decisions rather than sampling independent successors for each candidate action. This branchless graph structure exposes large GPU-friendly batches while using heuristics to focus computation. The authors prove finite-sample performance guarantees: under suitable overlap, regularity, and action coverage conditions, the bounds depend only polynomially on the planning horizon, formally characterizing when shared futures avoid the exponential horizon dependency of tree-based sparse sampling. In continuous control simulations, GSS substantially outperforms tree-based planners or achieves near-optimal performance over long horizons.
Paper Overview
Field: AI
Authors: Idan Lev-Yehudi, Vadim Indelman
Published: 2026-07-06
arXiv: 2607.05359
Abstract (translated)
Planning under uncertainty in continuous domains is essential for autonomous systems but is computationally demanding. Tree-search methods such as MCTS remain popular, yet their branching structure requires a sampling budget that grows exponentially with the lookahead depth in the worst case.
This paper proposes Graph Sparse Sampling (GSS), an online planning algorithm that shares sampled futures across multiple candidate decisions rather than sampling independent successors for each candidate action. This branchless graph exposes large GPU-friendly batches while using heuristics to focus computation.
The authors prove finite-sample performance guarantees for GSS. Under suitable overlap, regularity, and action coverage conditions, these bounds have a polynomial dependence on the planning horizon, formally characterizing when sharing futures can avoid the exponential horizon dependency of tree-based sparse sampling.
In continuous control simulations, GSS substantially outperforms tree-based planners over long horizons, or achieves near-optimal performance.
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*Auto-collected on 2026-07-06*
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