Home Reinforcement Learning and Artificial Intelligence (RLAI)
Reinforcement Learning Toolkit Wishlist
initial author: steph schaeffer

The ambition of this web page is to provide information about the intended contents of the RL toolkit contents. The toolkit is a collection of tools, examples and demos of and for reinforcement learning, developed by the RLAI group. It is hoped that these tools will be useful for those learning, teaching or using reinforcement learning.

Converted to python 3.5 on Oct 2016. -rss

RL Toolkit Wishlist

(contents of RL Toolkit 1.0 discussed later)

This is a bit of a wish list, but I hope the ambition it expresses will keep us from getting bogged down. And it is incomplete. you might consider adding some more wishes to it if they spring to mind. I hope you will also consider being more directly a part of creating the RL toolkit. If we work together we can not only get more done, but likely produce a better product as well, one that will be more useful to ourselves and to others.

RL Toolkit 1.0 Projected Contents

- assuming small # discrete actions, complete observability
- tabular, state aggregation, and linear function approximation -- Main.RichSutton - 14 Mar 2004

Extend this Page   How to edit   Style   Subscribe   Notify   Suggest   Help   This open web page hosted at the University of Alberta.   Terms of use  1766/1