Hidden Markov model(HMM) is partially observable Markov chains, i.e. observing a Markov chain through a noisy channel [24,75].
This forward model, in addition to a perceptual-range limiting observation function, is combined into a Partially Observable MDP (POMDP).
The transformations are partially observable by means of measurable parameters.
"Optimal Defense Policies for Partially Observable Spreading Processes on Bayesian Attack Graphs".
Topics include: Bayesian networks, influence diagrams, dynamic programming, reinforcement learning, and partially observable Markov decision processes.
Partially observable Markov decision processes, approximate dynamic programming, and reinforcement learning.
"Robust Partially Observable Markov Decision Processes". HKS Faculty Research Working Paper Series RWP18-027, September 2018.
Partially observable Markov decision processes (POMDPs) arise frequently in real-world problems.
My PhD focused on Bayesian nonparamtric methods for reinforcement learning in partially observable domains.
Ron Parr and Stuart Russell, ''Approximating Optimal Policies for Partially Observable Stochastic Domains". In Proc.
We study throughput utility maximization in a multi-user network with partially observable Markovian channels.
