A two-armed bandit based scheme for accelerated decentralized learning
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II
Optimistic Bayesian sampling in contextual-bandit problems
The Journal of Machine Learning Research
Uncertainty in online experiments with dependent data: an evaluation of bootstrap methods
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
LASER: a scalable response prediction platform for online advertising
Proceedings of the 7th ACM international conference on Web search and data mining
Personal and Ubiquitous Computing
Designing and deploying online field experiments
Proceedings of the 23rd international conference on World wide web
| Hi-index | 0.00 |
A multi-armed bandit is an experiment with the goal of accumulating rewards from a payoff distribution with unknown parameters that are to be learned sequentially. This article describes a heuristic for managing multi-armed bandits called randomized probability matching, which randomly allocates observations to arms according the Bayesian posterior probability that each arm is optimal. Advances in Bayesian computation have made randomized probability matching easy to apply to virtually any payoff distribution. This flexibility frees the experimenter to work with payoff distributions that correspond to certain classical experimental designs that have the potential to outperform methods that are ‘optimal’ in simpler contexts. I summarize the relationships between randomized probability matching and several related heuristics that have been used in the reinforcement learning literature. Copyright © 2010 John Wiley & Sons, Ltd.