Skip to content

Some material relating to the paper Comparing Direct and Indirect Temporal-Difference Methods for Estimating the Variance of the Return.

License

Notifications You must be signed in to change notification settings

dylanashley/variance-learning

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

1 Commit
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

variance-learning

This repository contains some material relating to the paper Comparing Direct and Indirect Temporal-Difference Methods for Estimating the Variance of the Return by Craig Sherstan, Dylan R. Ashley, Brendan Bennett, Kenny Young, Adam White, Martha White, and Richard S. Sutton. This paper was presented at the 2018 Conference on Uncertainty in Artificial Intelligence.

The following is included here:

  • src: the code used to generate the linear function approximation results in the paper
  • poster: the code used to generate the poster which accompanied the paper
  • presentation: the code used to generate one of the presentations which accompanied the paper

About

Some material relating to the paper Comparing Direct and Indirect Temporal-Difference Methods for Estimating the Variance of the Return.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published