Multi Agent Setting

Multi-agent settings research focuses on designing and analyzing systems where multiple autonomous agents interact, cooperate, or compete to achieve individual or collective goals. Current research emphasizes developing algorithms and architectures, such as multi-agent reinforcement learning (MARL) with techniques like Proximal Policy Optimization and Thompson Sampling, and leveraging large language models (LLMs) for improved strategic reasoning and communication in complex environments. This field is crucial for advancing artificial intelligence, particularly in areas like robotics, autonomous systems, and game theory, by enabling the creation of more robust and adaptable intelligent systems capable of handling real-world challenges.