Learning is planning: near Bayes-optimal reinforcement learning via Monte-Carlo tree search

Details Learning is planning: near Bayes-optimal reinforcement learning via Monte-Carlo tree search Learning is planning: near Bayes-optimal reinforcement learning via Monte-Carlo tree search

2012-02-14

arxiv.org/abs/1202.3699v1

Computer Science

Artificial Intelligence

Scientific paper

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 near-optimally 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. We show how an agent can use one particular MCTS algorithm, Forward Search Sparse Sampling (FSSS), in an efficient way to act nearly Bayes-optimally for all but a polynomial number of steps, assuming that FSSS can be used to act efficiently in any possible underlying MDP.

Affiliated with

Also associated with

No associations

Rating

Learning is planning: near Bayes-optimal reinforcement learning via Monte-Carlo tree search does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Learning is planning: near Bayes-optimal reinforcement learning via Monte-Carlo tree search, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Learning is planning: near Bayes-optimal reinforcement learning via Monte-Carlo tree search will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-90325