This lecture focuses on policy-based reinforcement learning, emphasizing methods that optimize parameterized policies to maximize expected rewards without explicitly defining a value function. It highlights the benefits of stochastic policies in dealing with non-Markov processes and partial observability, using engaging examples like Rock-Paper-Scissors and robotics. At the heart of this discussion is the policy gradient theorem, which allows for the calculation of policy gradients through the likelihood ratio and score function, leading to algorithms such as REINFORCE. Although these methods initially exhibit high variance, strategies like introducing baselines help enhance efficiency and lower variance in gradient estimation. This foundation sets the stage for a deeper exploration of advanced algorithms like Proximal Policy Optimization (PPO).
