TY - GEN
T1 - Cooperation-eliciting Prisoner's dilemma payoffs for reinforcement learning agents
AU - Moriyama, Koichi
AU - Kurihara, Satoshi
AU - Numao, Masayuki
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - Game theory
KW - Reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=84911404219&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84911404219&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84911404219
T3 - 13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014
SP - 1619
EP - 1620
BT - 13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014
PB - International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
T2 - 13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014
Y2 - 5 May 2014 through 9 May 2014
ER -