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Strengthening Deterministic Policies for POMDPs
Second, based on the results of the original MILP, we employ a preprocessing of the POMDP to encompass memory-based decisions.
Counterexample-Guided Strategy Improvement for POMDPs Using Recurrent Neural Networks
The particular problem is to determine strategies that provably adhere to (probabilistic) temporal logic constraints.
Human-in-the-Loop Synthesis for Partially Observable Markov Decision Processes
The efficient verification of this MC gives quantitative insights into the quality of the inferred human strategy by proving or disproving given system specifications.
Strategy Synthesis in POMDPs via Game-Based Abstractions
We study synthesis problems with constraints in partially observable Markov decision processes (POMDPs), where the objective is to compute a strategy for an agent that is guaranteed to satisfy certain safety and performance specifications.
