13 papers
Graph Sparse Sampling: Breaking the Curse of the Horizon in Continuous MDP Planning
Idan Lev-Yehudi, Vadim Indelman
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…
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…
Finite-Time Analysis of MCTS in Continuous POMDP Planning
Da Kong, Vadim Indelman
This paper presents a finite-time analysis for Monte Carlo Tree Search (MCTS) in Partially Observable Markov Decision Processes (POMDPs), with probabilistic concentration bounds in…
Open-loop POMDP Simplification and Safe Skipping of Replanning with Formal Performance Guarantees
Da Kong, Vadim Indelman
Partially Observable Markov Decision Processes (POMDPs) provide a principled mathematical framework for decision-making under uncertainty. However, the exact solution to POMDPs is…
Accelerated Online Risk-Averse Policy Evaluation in POMDPs with Theoretical Guarantees and Novel CVaR Bounds
Yaacov Pariente, Vadim Indelman
Risk-averse decision-making under uncertainty in partially observable domains is a central challenge in artificial intelligence and is essential for developing reliable autonomous…
Online Risk-Averse Planning in POMDPs Using Iterated CVaR Value Function
Yaacov Pariente, Vadim Indelman
We study risk-sensitive planning under partial observability using the dynamic risk measure Iterated Conditional Value-at-Risk (ICVaR). A policy evaluation algorithm for ICVaR is d…