Summary
Researchers Idan Lev-Yehudi and Vadim Indelman propose Graph Sparse Sampling (GSS), an online planning algorithm for decision-making under uncertainty in continuous domains. Unlike tree-search methods such as MCTS, which sample independent futures per candidate action and thus require exponentially growing sampling budgets with lookahead depth, GSS shares sampled futures across multiple candidate decisions, forming a branching-free graph. This 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 over long horizons or achieves near-optimal performance. The work was released on arXiv as 2607.05359.
Paper Overview
Field: AI
Authors: Idan Lev-Yehudi, Vadim Indelman
Published: 2026-07-06
arXiv:
2607.05359Abstract (translated from the forum post)
Planning under uncertainty in continuous domains is critical for autonomous systems, but it is computationally demanding. Tree-search methods such as MCTS remain popular, yet their branching structure requires, in the worst case, a sampling budget that grows exponentially with the lookahead depth. 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 branching-free 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 depend polynomially on the planning horizon, formally characterizing when shared futures can avoid the exponential horizon dependency inherent in 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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*Automatically collected on 2026-07-06*
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