Partially observable Markov decision processes and performance sensitivity analysis
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New framework synthesizes POMDP policies using sampling and model-checking
Researchers have developed a new framework to synthesize policies for Partially Observable Markov Decision Processes (POMDPs), which are used for decision-making under uncertainty. This approach combines sampling-based …
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New MCTS analysis offers theoretical guarantees for POMDP planning
Researchers have developed a new finite-time analysis for Monte Carlo Tree Search (MCTS) when applied to Partially Observable Markov Decision Processes (POMDPs). This work provides probabilistic concentration bounds for…