Approaching Bayes-optimalilty using Monte-Carlo tree search

@inproceedings{Asmuth2011ApproachingBU,
  title={Approaching Bayes-optimalilty using Monte-Carlo tree search},
  author={John Asmuth and Michael S. Littman},
  year={2011}
}
Bayes-optimal behavior, while well-defined, is often difficult to achieve. Recent advances in the use of Monte-Carlo tree search (MCTS) have shown that it is possible to act nearoptimally in Markov Decision Processes (MDPs) with very large or infinite state spaces. Bayes-optimal behavior in an unknown MDP is equivalent to optimal behavior in the known belief-space MDP, although the size of this belief-space MDP grows exponentially with the amount of history retained, and is potentially infinite… CONTINUE READING
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