Considering Unseen States as Impossible in Factored Reinforcement Learning
published: Oct. 20, 2009, recorded: September 2009, views: 2754
Slides
Related content
Report a problem or upload files
If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Description
The Factored Markov Decision Process (FMDP) framework is a standard representation for sequential decision problems under uncertainty where the state is represented as a collection of random variables. Factored Reinforcement Learning (FRL) is an Model-based Reinforcement Learning approach to FMDPs where the transition and reward unctions of the problem are learned. In this paper, we show how to model in a theoretically well-founded way the problems where some combinations of state variable values may not occur, giving rise to impossible states. Furthermore, we propose a new heuristics that considers as impossible the states that have not been seen so far. We derive an algorithm whose improvement in performance with respect to the standard approach is illustrated through benchmark experiments.
Link this page
Would you like to put a link to this lecture on your homepage?Go ahead! Copy the HTML snippet !
Write your own review or comment: