Cooperation lies at the core of multiagent systems (MAS) and multiagent
reinforcement learning (MARL), where agents must navigate between individual
interests and collective benefits. Advanced driver assistance systems (ADAS),
like collision avoidance systems and adaptive cruise control, exemplify agents
striving to optimize personal and collective outcomes in multiagent
environments. The study focuses on strategies aimed at fostering cooperation
with the aid of game-theoretic scenarios, particularly the iterated prisoner’s
dilemma, where agents aim to optimize personal and group outcomes. Existing
cooperative strategies, such as tit-for-tat and win-stay lose-shift, while
effective in certain contexts, often struggle with scalability and adaptability
in dynamic, large-scale environments. The research investigates these
limitations and proposes modifications to align individual gains with collective
rewards, addressing real-world dilemmas in distributed systems. By analyzing
existing cooperative strategies, the research investigates their effectiveness
in encouraging group-oriented behavior in repeated games. It suggests
modifications to align individual gains with collective rewards, addressing
real-world dilemmas in distributed systems. Furthermore, it extends to scenarios
with exponentially growing agent populations (N → +∞),
addressing computational challenges using mean-field game theory to establish
equilibrium solutions and reward structures tailored for infinitely large agent
sets. Practical insights are provided by adapting simulation algorithms to
create scenarios conducive to cooperation for group rewards. Additionally, the
research advocates for incorporating vehicular behavior as a metric to assess
the induction of cooperation, bridging theoretical constructs with real-world
applications.
