Multi Agent Policy

Multi-agent policy research focuses on developing algorithms that enable multiple agents to learn and coordinate their actions effectively to achieve a shared goal, often within complex and dynamic environments. Current research emphasizes improving the efficiency and robustness of these algorithms, exploring architectures like transformers and Bayesian networks, and employing techniques such as policy gradient methods, actor-critic models, and various forms of decentralized training. This field is crucial for advancing autonomous systems, particularly in areas like robotics, traffic control, and resource management, where coordinated multi-agent behavior is essential for optimal performance and safety. Significant efforts are also dedicated to addressing challenges like credit assignment, non-stationarity, and robustness to adversarial attacks.