  We study the evolution of behavior under reinforcement learning in a
Prisoner's Dilemma where agents interact in a regular network and can learn
about whether they play one-shot or repeatedly by incurring a cost of
deliberation. With respect to other behavioral rules used in the literature,
(i) we confirm the existence of a threshold value of the probability of
repeated interaction, switching the emergent behavior from intuitive defector
to dual-process cooperator; (ii) we find a different role of the node degree,
with smaller degrees reducing the evolutionary success of dual-process
cooperators; (iii) we observe a higher frequency of deliberation.
