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  1. Value-Function Approximations for Partially Observable Markov Decision Processes

  2. Value-Function Approximations for Partially Observable Markov ...

  3. Value-Function Approximations for Partially Observable Markov ...

  4. Value-Function Approximations for Partially Observable Markov …

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    In many problem domains, however, an agent suffers from limited sensing capabilities that preclude it from recovering a Markovian state signal from its perceptions. Extending the MDP framework, partially observable Markov decision processes (POMDPs) allow for principled decision making under conditions of uncertain sensing.
    link.springer.com/content/pdf/10.1007/978-3-642-27645-…
    For reinforcement learning in environments in which an agent has access to a reliable state signal, methods based on the Markov decision process (MDP) have had many successes. In many problem domains, however, an agent suffers from limited sensing capabilities that preclude it from recovering a Markovian state signal from its perceptions.
    link.springer.com/content/pdf/10.1007/978-3-642-27645-…
    Partially observable markov decision processes for prostate cancer screening, surveillance, and treatment: A budgeted sampling approximation method. Decision Analyt-ics and Optimization in Disease Prevention and Treatment, pages 201{222. Zymler, S., Kuhn, D., and Rustem, B. (2013).
    arxiv.org/pdf/1906.05988.pdf
    This corresponds to a POMDP where there is uncertainty about the state In a POMDP the current state is not known with certainty, only the probability distribution of the state, which is known as the belief state. Thus, since the states are not directly observable, the action selection has to be based on the past observations.
    link.springer.com/chapter/10.1007/978-3-030-61943-5_12
  6. Partially Observable Markov Decision Processes | SpringerLink

  7. Partially Observable Markov Decision Processes | SpringerLink