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

Koichi Moriyama, Satoshi Kurihara, Masayuki Numao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages1619-1620
Number of pages2
ISBN (Electronic)9781634391313
Publication statusPublished - 2014
Event13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014 - Paris, France
Duration: 2014 May 52014 May 9

Publication series

Name13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014
Volume2

Other

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

Keywords

  • Game theory
  • Reinforcement learning

ASJC Scopus subject areas

  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Cooperation-eliciting Prisoner's dilemma payoffs for reinforcement learning agents'. Together they form a unique fingerprint.

  • Cite this

    Moriyama, K., Kurihara, S., & Numao, M. (2014). Cooperation-eliciting Prisoner's dilemma payoffs for reinforcement learning agents. In 13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014 (pp. 1619-1620). (13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014; Vol. 2). International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).