  This paper studies multi-agent systems that involve networks of
self-interested agents. We propose a Markov Decision Process-derived framework,
called RepNet-MDP, tailored to domains in which agent reputation is a key
driver of the interactions between agents. The fundamentals are based on the
principles of RepNet-POMDP, a framework developed by Rens et al. in 2018, but
addresses its mathematical inconsistencies and alleviates its intractability by
only considering fully observable environments. We furthermore use an online
learning algorithm for finding approximate solutions to RepNet-MDPs. In a
series of experiments, RepNet agents are shown to be able to adapt their own
behavior to the past behavior and reliability of the remaining agents of the
network. Finally, our work identifies a limitation of the framework in its
current formulation that prevents its agents from learning in circumstances in
which they are not a primary actor.
