By Topic

An Acquiring Method of Macro-Actions in 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
$33 $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

2 Author(s)
Yoshikawa, T. ; Grad. Sch. of Inf. Sci. & Technol., Hokkaido Univ., Sapporo ; Kurihara, M.

Reinforcement learning is a framing of enabling agents to learn from interaction with environments. It has focused generally on Markov decision process (MDP) domains, but a domain may be non-Markovian in the real world. In this paper, we introduce a new description of macro-actions with tree structure in reinforcement learning. The macro-action is an action control structure which provides an agent with control which applies a collection of related microscopic actions as a single action unit. And we propose a simple method for dynamically acquiring macro-actions from the experiences of agents during reinforcement learning process.

Published in:

Systems, Man and Cybernetics, 2006. SMC '06. IEEE International Conference on  (Volume:6 )

Date of Conference:

8-11 Oct. 2006