A layered approach to learning coordination knowledge in multiagent environments

Erus G., Polat F.

APPLIED INTELLIGENCE, cilt.27, sa.3, ss.249-267, 2007 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 27 Sayı: 3
  • Basım Tarihi: 2007
  • Doi Numarası: 10.1007/s10489-006-0034-y
  • Dergi Adı: APPLIED INTELLIGENCE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.249-267
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

Multiagent learning involves acquisition of cooperative behavior among intelligent agents in order to satisfy the joint goals. Reinforcement Learning (RL) is a promising unsupervised machine learning technique inspired from the earlier studies in animal learning. In this paper, we propose a new RL technique called the Two Level Reinforcement Learning with Communication (2LRL) method to provide cooperative action selection in a multiagent environment. In 2LRL, learning takes place in two hierarchical levels; in the first level agents learn to select their target and then they select the action directed to their target in the second level. The agents communicate their perception to their neighbors and use the communication information in their decision-making. We applied 2LRL method in a hunter-prey environment and observed a satisfactory cooperative behavior.