Explore the challenges and lessons learned in partner modeling for decentralized multi-agent coordination in this 55-minute lecture by Dorsa Sadigh from Stanford University. Delve into the role of representation learning in developing effective conventions and latent partner strategies, and discover how to leverage these conventions within reinforcement learning loops for coordination, collaboration, and influencing. Examine strategies for stabilizing latent partner representations to reduce non-stationarity and achieve more desirable learning outcomes. Investigate the formalization of decentralized multi-agent coordination as a collaborative multi-armed bandit with partial observability, and learn how partner modeling strategies can achieve logarithmic regret. Gain insights into autonomous driving, coordination tasks, and collaborative robotics while exploring topics such as intrinsic motivation and the superiority of human intelligence in complex scenarios.