Help Privacy Policy Disclaimer
  Advanced SearchBrowse




Journal Article

Structure in the Space of Value Functions

There are no MPG-Authors in the publication available
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available

Foster, D., & Dayan, P. (2002). Structure in the Space of Value Functions. Machine Learning, 49(2-3), 325-346. doi:10.1023/A:1017944732463.

Cite as: https://hdl.handle.net/21.11116/0000-0002-D4DC-7
Solving in an efficient manner many different optimal control tasks within the same underlying environment requires decomposing the environment into its computationally elemental fragments. We suggest how to find fragmentations using unsupervised, mixture model, learning methods on data derived from optimal value functions for multiple tasks, and show that these fragmentations are in accord with observable structure in the environments. Further, we present evidence that such fragments can be of use in a practical reinforcement learning context, by facilitating online, actor-critic learning of multiple goals MDPs.