This podcast explores policy gradient methods in reinforcement learning, emphasizing how to enhance sample efficiency and stability. Key topics include the role of baselines in unbiased gradient estimation, alternative targets like Q-functions that help reduce variance, and sophisticated techniques like Proximal Policy Optimization (PPO). PPO improves upon basic policy gradients by introducing methods to limit policy changes through KL divergence and clipping, allowing for multiple gradient updates from a single data collection. This approach not only accelerates convergence but also boosts sample efficiency significantly. The conversation also underscores the need to find a balance between bias and variance when estimating value functions, along with the trade-offs that come with different n-step estimators.
