Two Agent

Two-agent systems research focuses on understanding and optimizing interactions between two artificial agents, exploring scenarios ranging from competition to cooperation. Current research emphasizes developing algorithms, such as those based on game theory (e.g., Stackelberg games, Nash equilibria) and reinforcement learning (e.g., fictitious play, policy space response oracles), to model and improve agent behavior in diverse contexts. These advancements are significant for improving the robustness and adaptability of AI systems in multi-agent environments, with applications ranging from autonomous robotics to safe and efficient human-AI collaboration.