Multi Agent Task

Multi-agent task research focuses on enabling multiple agents to collaboratively achieve a shared objective, overcoming challenges like credit assignment and non-stationarity inherent in multi-agent systems. Current research emphasizes improving coordination through techniques such as attention mechanisms for reward decoupling, leveraging large language models for theory of mind capabilities, and developing novel algorithms like those based on deep Q-networks and tree search for efficient policy learning in both cooperative and competitive settings. These advancements are significant for improving the scalability and robustness of multi-agent systems, with potential applications ranging from robotics and autonomous driving to complex simulations and game playing.