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A Bayesian Approach for Learning and Planning in Partially Observable Markov Decision Processes.
Stéphane Ross
Joelle Pineau
Brahim Chaib-draa
Pierre Kreitmann
Published in:
J. Mach. Learn. Res. (2011)
Keyphrases
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stochastic domains
partially observable markov decision processes
predictive state representations
reinforcement learning
learning algorithm
continuous state
dynamical systems
planning under uncertainty
multi agent
dynamic programming
state space
state variables
finite state
belief space