Graph Sparse Sampling: Breaking the Curse of the Horizon in Continuous MDP Planning
By Idan Lev-Yehudi · Paper · cs.AI
Planning under uncertainty in continuous domains is essential for autonomous systems, yet computationally demanding. Tree-based search methods such as Monte Carlo Tree Search (MCTS) remain popular, but their branching structure can require sampling budgets that grow exponentially