A Reinforcement Learning Method using TD-Error in Ant Colony System 


Vol. 11,  No. 1, pp. 77-82, Feb.  2004
10.3745/KIPSTB.2004.11.1.77


PDF
  Abstract

Reinforcement learning takes reward about selecting action when agent chooses some action and did state transition in present state, this can be the important subject in reinforcement learning as temporal-credit assignment problems. In this paper, by new meta heuristic method to solve hard combinational optimization problem, examine Ant-Q learning method that is proposed to solve Traveling Salesman Problem(TSP)to approach that is based for population that use positive feedback as well as greedy search. And, suggest Ant-TD reinforcement learning method that apply state transition through diversification strategy to this method and TD-error. We can show through experiments that the reinforcement learning method proposed in this paper can find out an optimal solution faster than other reinforcement learning method like ACS and Ant-Q learning.

  Statistics
  Cite this article

[IEEE Style]

L. S. Gwan and J. T. Chung, "A Reinforcement Learning Method using TD-Error in Ant Colony System," The KIPS Transactions:PartB , vol. 11, no. 1, pp. 77-82, 2004. DOI: 10.3745/KIPSTB.2004.11.1.77.

[ACM Style]

Lee Seung Gwan and Jeong Tae Chung. 2004. A Reinforcement Learning Method using TD-Error in Ant Colony System. The KIPS Transactions:PartB , 11, 1, (2004), 77-82. DOI: 10.3745/KIPSTB.2004.11.1.77.

Forms

Search
(IN TITLE, AUTHOR, ABSTRACT,KEYWORDS)

Advanced Search ▼

POPULAR KEYWORDS
(TOP 10 KEYWORDS)

Recent Publications
(LAST 3 YEARS)

Old Journals

Indexing

All publications of TKIPS is indexed in DOI, EBSCO, Google Scholar, Crossref, and CrossCheck.

TKIPS is also selected as the Journal for Accreditation by NRF (National Research Foundation of Korea).

Related Journals