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cs.AI2025
Tighter Value-Function Approximations for POMDPs
Merlijn Krale, Wietze Koops, Sebastian Junges +2
Solving partially observable Markov decision processes (POMDPs) typically requires reasoning about the values of exponentially many state beliefs. Towards practical performance, st…
cs.AI2023★ 1 cited
Act-Then-Measure: Reinforcement Learning for Partially Observable Environments with Active Measuring
Merlijn Krale, Thiago D. Simão, Nils Jansen
We study Markov decision processes (MDPs), where agents have direct control over when and how they gather information, as formalized by action-contingent noiselessly observable MDP…