I study how agents can learn to act in complex, multi-agent environments with minimal experience by exploiting structure in the underlying decision problem. My interests are at the intersection of reinforcement learning, control theory, and multi-agent systems. I focus on sample complexity reduction through latent structure in MDPs, including low-rank Q-functions, spectral gaps, and manifold geometry.