Belief propagation is a popular global optimization technique for many computer vision problems. However, it requires extensive computation due to the iterative message passing op...
Abstract— Research on numerical solution methods for partially observable Markov decision processes (POMDPs) has primarily focused on discrete-state models, and these algorithms ...
A stochastic graph game is played by two players on a game graph with probabilistic transitions. We consider stochastic graph games with -regular winning conditions specified as Ra...
Decentralized partially observable Markov decision processes (DEC-POMDPs) form a general framework for planning for groups of cooperating agents that inhabit a stochastic and part...
Matthijs T. J. Spaan, Geoffrey J. Gordon, Nikos A....
The problem of reinforcement learning in large factored Markov decision processes is explored. The Q-value of a state-action pair is approximated by the free energy of a product o...