Ron Benchetrit — accepted AI-conference papers

2026

Action-Gradient Monte Carlo Tree Search for Non-Parametric Continuous (PO)MDPs

Idan Lev-Yehudi, Michael Novitsky, Moran Barenboim, Ron Benchetrit, Vadim Indelman

IJCAI 2026

Online planning in continuous state, action, and observation spaces remains challenging for autonomous systems. While Monte Carlo Tree Search (MCTS) scales effectively via sampling, most continuous (PO)MDP solvers do not exploit gradient-based action optimization. We propose Action-Gradient MCTS (AG

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2026

Online Robust Planning Under Model Uncertainty: A Sample-Based Approach

Tamir Shazman, Idan Lev-Yehudi, Ron Benchetrit, Vadim Indelman

AAAI 2026technical

Online planning in Markov Decision Processes (MDPs) enables agents to make sequential decisions by simulating future trajectories from the current state, making it well-suited for large-scale or dynamic environments. Sample-based methods such as Sparse Sampling and Monte Carlo Tree Search (MCTS) are

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