Explore multiagent reinforcement learning through a 37-minute lecture by Dimitri Bertsekas from ASU & MIT, focusing on rollout and policy iteration techniques. Delve into finite-state infinite horizon problems, the Policy Iteration (PI) algorithm, and the underlying theory of trading off control and state complexity. Compare standard and multiagent approaches to rollout and policy iteration, and examine approximate policy iteration with agent-by-agent policy improvement. Gain insights into this well-researched field dating back to the 1960s, presented as part of the Simons Institute's series on reinforcement learning from batch data and simulation.