Joint Policy

Joint policy in multi-agent reinforcement learning (MARL) focuses on coordinating the actions of multiple agents to achieve a shared goal, a challenge amplified by the exponentially growing search space as the number of agents increases. Current research emphasizes developing efficient algorithms, such as those based on policy gradient methods and auto-regressive models, to learn optimal or near-optimal joint policies, often addressing issues like relative overgeneralization and the trade-off between centralized training and decentralized execution. This research is significant for advancing the capabilities of MARL in complex scenarios, with potential applications ranging from robotics and game playing to resource management and distributed control systems.