  Cooperation is fundamental in Multi-Agent Systems (MAS) and Multi-Agent
Reinforcement Learning (MARL), often requiring agents to balance individual
gains with collective rewards. In this regard, this paper aims to investigate
strategies to invoke cooperation in game-theoretic scenarios, namely the
Iterated Prisoner's Dilemma, where agents must optimize both individual and
group outcomes. Existing cooperative strategies are analyzed for their
effectiveness in promoting group-oriented behavior in repeated games.
Modifications are proposed where encouraging group rewards will also result in
a higher individual gain, addressing real-world dilemmas seen in distributed
systems. The study extends to scenarios with exponentially growing agent
populations ($N \longrightarrow +\infty$), where traditional computation and
equilibrium determination are challenging. Leveraging mean-field game theory,
equilibrium solutions and reward structures are established for infinitely
large agent sets in repeated games. Finally, practical insights are offered
through simulations using the Multi Agent-Posthumous Credit Assignment trainer,
and the paper explores adapting simulation algorithms to create scenarios
favoring cooperation for group rewards. These practical implementations bridge
theoretical concepts with real-world applications.
