Stanford CS224R Deep Reinforcement Learning | Spring 2025 | Lecture 2: Imitation Learning

The podcast discusses imitation learning, a method for training policies by mimicking expert demonstrations. It covers representing policy distributions using neural networks, emphasizing the importance of expressive distributions to capture the multimodality often present in expert data. The discussion includes techniques like Gaussian mixture models, discretized actions with autoregressive models, and diffusion models. The podcast addresses challenges such as compounding errors and covariate shift, and it introduces strategies for collecting corrective data through online interventions like the DAgger algorithm to improve policy robustness.