Explore a 58-minute lecture on swarm intelligence and multi-agent ecosystems in artificial intelligence, focusing on decentralized control systems and their real-world applications. Dive into the complexities of multi-agent reinforcement learning (MARL) and discover how asynchronous systems break traditional synchronization barriers in urban traffic and drone networks. Learn about subgraph decomposition techniques that enable scalable real-time optimization across large environments, and understand how Partially Observable Markov Decision Processes (POMDP) allow agents to make optimal decisions with incomplete information. Master the principles of gradient ascent in autonomous decision-making, examining how drone transportation networks can self-optimize without centralized oversight. Through detailed examples and theoretical frameworks, gain insights into emerging technologies that enable collective intelligence in urban environments, from traffic management to drone swarm coordination. Read more