Learning intelligent behavior in a non-stationary and partially observable environment

Senkul S., Polat F.

ARTIFICIAL INTELLIGENCE REVIEW, cilt.18, sa.2, ss.97-115, 2002 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 18 Sayı: 2
  • Basım Tarihi: 2002
  • Doi Numarası: 10.1023/a:1019935502139
  • Dergi Adı: ARTIFICIAL INTELLIGENCE REVIEW
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.97-115
  • Anahtar Kelimeler: agent learning, multi-agent systems, Q-learning, reinforcement learning, REINFORCEMENT
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

Individual learning in an environment where more than one agent exist is a challenging task. In this paper, a single learning agent situated in an environment where multiple agents exist is modeled based on reinforcement learning. The environment is non-stationary and partially accessible from an agents' point of view. Therefore, learning activities of an agent is influenced by actions of other cooperative or competitive agents in the environment. A prey-hunter capture game that has the above characteristics is defined and experimented to simulate the learning process of individual agents. Experimental results show that there are no strict rules for reinforcement learning. We suggest two new methods to improve the performance of agents. These methods decrease the number of states while keeping as much state as necessary.