By Topic

Exception-based reinforcement learning

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Garcia, P. ; Departement Informatique, IRISA/INSA, Rennes, France

In this paper we develop a method using temporally abstract actions to solve Markov decision processes. The basic idea of our method is to define some kind of procedures to control the agent's behavior. These procedures contain a rule constraining actions the agent has to choose. This rule is applied except if some conditions (which we call exceptions) are fulfilled. In this case we relax constraints on actions. We develop a way to propagate states that have created an exception to a rule, to help the agent to escape from blocked situations or locally optimal solutions. We illustrate the method using the "Sokoban" game. We compare the method empirically with flat Q-learning. On the proposed tests, learning time is drastically reduced as is the memory required to save the Q-values

Published in:

Industrial Electronics Society, 2001. IECON '01. The 27th Annual Conference of the IEEE  (Volume:3 )

Date of Conference:

2001