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cs.AI2024★ 2 cited
Learning Explainable and Better Performing Representations of POMDP Strategies
Alexander Bork, Debraj Chakraborty, Kush Grover +2
Strategies for partially observable Markov decision processes (POMDP) typically require memory. One way to represent this memory is via automata. We present a method to learn an au…
cs.AI2023★ 1 cited
MULTIGAIN 2.0: MDP controller synthesis for multiple mean-payoff, LTL and steady-state constraints
Severin Bals, Alexandros Evangelidis, Jan Křetínský +1
We present MULTIGAIN 2.0, a major extension to the controller synthesis tool MULTIGAIN, built on top of the probabilistic model checker PRISM. This new version extends MULTIGAIN's…