Factored Value Iteration Converges

Details Factored Value Iteration Converges Factored Value Iteration Converges

2008-01-14

arxiv.org/abs/0801.2069v2

Computer Science

Artificial Intelligence

17 pages, 1 figure

Scientific paper

In this paper we propose a novel algorithm, factored value iteration (FVI), for the approximate solution of factored Markov decision processes (fMDPs). The traditional approximate value iteration algorithm is modified in two ways. For one, the least-squares projection operator is modified so that it does not increase max-norm, and thus preserves convergence. The other modification is that we uniformly sample polynomially many samples from the (exponentially large) state space. This way, the complexity of our algorithm becomes polynomial in the size of the fMDP description length. We prove that the algorithm is convergent. We also derive an upper bound on the difference between our approximate solution and the optimal one, and also on the error introduced by sampling. We analyze various projection operators with respect to their computation complexity and their convergence when combined with approximate value iteration.

Affiliated with

Also associated with

No associations

Rating

Factored Value Iteration Converges 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 Factored Value Iteration Converges, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Factored Value Iteration Converges will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-634317