4 papers
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…
Online POMDP Planning with Anytime Deterministic Optimality Guarantees
Moran Barenboim, Vadim Indelman
Decision-making under uncertainty is a critical aspect of many practical autonomous systems due to incomplete information. Partially Observable Markov Decision Processes (POMDPs) o…
Simplifying Complex Observation Models in Continuous POMDP Planning with Probabilistic Guarantees and Practice
Idan Lev-Yehudi, Moran Barenboim, Vadim Indelman
Solving partially observable Markov decision processes (POMDPs) with high dimensional and continuous observations, such as camera images, is required for many real life robotics an…
Previous Knowledge Utilization In Online Anytime Belief Space Planning
Michael Novitsky, Moran Barenboim, Vadim Indelman
Online planning under uncertainty remains a critical challenge in robotics and autonomous systems. While tree search techniques are commonly employed to construct partial future tr…