Stanford CS224R Deep Reinforcement Learning | Spring 2025 | Lecture 7: Offline RL

The podcast transcript details a lecture on reinforcement learning (RL), specifically focusing on offline RL methods. It begins with a recap of online RL, covering policy gradients, value functions, and actor-critic approaches like PPO and SAC. The lecture transitions to different methods for fitting value functions, including Monte Carlo returns and temporal difference (TD) updates. It discusses Q-functions and their use in off-policy learning, highlighting the importance of action coverage. The core of the lecture then shifts to offline reinforcement learning, addressing the challenges of learning from a fixed dataset without online data collection. The discussion covers imitation learning, advantage-weighted regression, and implicit Q-learning (IQL), emphasizing techniques to mitigate overestimation and improve upon behavior policies, including the use of expectile regression.