  Humans and other animals can adapt their social behavior in response to
environmental cues including the feedback obtained through experience.
Nevertheless, the effects of the experience-based learning of players in
evolution and maintenance of cooperation in social dilemma games remain
relatively unclear. Some previous literature showed that mutual cooperation of
learning players is difficult or requires a sophisticated learning model. In
the context of the iterated Prisoner's Dilemma, we numerically examine the
performance of a reinforcement learning model. Our model modifies those of
Karandikar et al. (1998), Posch et al. (1999), and Macy and Flache (2002) in
which players satisfice if the obtained payoff is larger than a dynamic
threshold. We show that players obeying the modified learning mutually
cooperate with high probability if the dynamics of threshold is not too fast
and the association between the reinforcement signal and the action in the next
round is sufficiently strong. The learning players also perform efficiently
against the reactive strategy. In evolutionary dynamics, they can invade a
population of players adopting simpler but competitive strategies. Our version
of the reinforcement learning model does not complicate the previous model and
is sufficiently simple yet flexible. It may serve to explore the relationships
between learning and evolution in social dilemma situations.
