GSS replaces tree-based MCTS with shared successor state layers and SNIS backups, proving polynomial horizon sample complexity under density overlap conditions.
Monte Carlo tree search in continuous spaces using Voronoi optimistic optimization with regret bounds
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Graph Sparse Sampling: Breaking the Curse of the Horizon in Continuous MDP Planning
GSS replaces tree-based MCTS with shared successor state layers and SNIS backups, proving polynomial horizon sample complexity under density overlap conditions.