Cooperation-eliciting Prisoner's dilemma payoffs for reinforcement learning agents

Koichi Moriyama, Satoshi Kurihara, Masayuki Numao

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

This work considers a stateless Q-learning agent in iterated Prisoner's Dilemma (PD). We have already given a condition of PD payoffs and Q-learning parameters that helps stateless Q-learning agents cooperate with each other [2]. That condition, however, has a restrictive premise. This work relaxes the premise and shows a new payoff condition for mutual cooperation. After that, we derive the payoff relations that will elicit mutual cooperation from the new condition.

本文言語English
ホスト出版物のタイトル13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014
出版社International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
ページ1619-1620
ページ数2
ISBN(電子版)9781634391313
出版ステータスPublished - 2014
外部発表はい
イベント13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014 - Paris, France
継続期間: 2014 5 52014 5 9

出版物シリーズ

名前13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014
2

Other

Other13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014
国/地域France
CityParis
Period14/5/514/5/9

ASJC Scopus subject areas

  • 人工知能

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