Nearly optimal exploration-exploitation decision thresholds

Details Nearly optimal exploration-exploitation decision thresholds Nearly optimal exploration-exploitation decision thresholds

2006-04-05

arxiv.org/abs/cs/0604010v1

Computer Science

Artificial Intelligence

10 pages, 2 figures; IDIAP Research Report

Scientific paper

While in general trading off exploration and exploitation in reinforcement learning is hard, under some formulations relatively simple solutions exist. Optimal decision thresholds for the multi-armed bandit problem, one for the infinite horizon discounted reward case and one for the finite horizon undiscounted reward case are derived, which make the link between the reward horizon, uncertainty and the need for exploration explicit. From this result follow two practical approximate algorithms, which are illustrated experimentally.

Affiliated with

Also associated with

No associations

Rating

Nearly optimal exploration-exploitation decision thresholds 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 Nearly optimal exploration-exploitation decision thresholds, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Nearly optimal exploration-exploitation decision thresholds will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-418471