Communication Learning

Communication learning in multi-agent systems focuses on enabling agents to effectively share information and coordinate actions, improving performance in complex tasks where individual agents lack complete environmental awareness. Current research emphasizes developing robust communication strategies using reinforcement learning, often incorporating techniques like optimistic online mirror descent or Q-network architectures, to address challenges such as noisy or adversarial communication channels and bandwidth limitations. This field is significant for advancing the capabilities of multi-agent systems in diverse applications, including robotics, autonomous driving, and distributed control, by enabling efficient collaboration and improved decision-making in dynamic environments.