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cs.AI2025
Action-Gradient Monte Carlo Tree Search for Non-Parametric Continuous (PO)MDPs
Idan Lev-Yehudi, Michael Novitsky, Moran Barenboim +2
Online planning in continuous state, action, and observation spaces remains challenging for autonomous systems. While Monte Carlo Tree Search (MCTS) scales effectively via sampling…
cs.AI2025
Anytime Incremental POMDP Planning in Continuous Spaces
Ron Benchetrit, Idan Lev-Yehudi, Andrey Zhitnikov +1
Partially Observable Markov Decision Processes (POMDPs) provide a robust framework for decision-making under uncertainty in applications such as autonomous driving and robotic expl…