Paper Info
Title
Improving PAC Exploration Using the Median Of Means.
Abstract
We present the first application of the median of means in a PAC exploration algorithm for MDPs. Using the median of means allows us to significantly reduce the dependence of our bounds on the range of values that the value function can take, while introducing a dependence on the (potentially much smaller) variance of the Bellman operator. Additionally, our algorithm is the first algorithm with PAC bounds that can be applied to MDPs with unbounded rewards.
Year
Venue
Field
2016
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 29 (NIPS 2016)
Mathematical optimization,Computer science,Algorithm,Bellman equation,Artificial intelligence,Operator (computer programming),Machine learning
DocType
Volume
ISSN
Conference
29
1049-5258
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Jason Pazis11046.97
Ronald Parr22428186.85
Jonathan How31759185.09