Policy Gradient Evaluation: Example (1-step horizon)

This lecture covers the evaluation of policy gradients using an example with a 1-step horizon, discussing the binary actor's online rule found rapidly with the log-likelihood trick and exploring various interpretations of the resulting rule. It also delves into the update rule of the example, comparing it with the Perceptron model and relating it to biology by analyzing the weight vector's direction in response to stimuli. Additionally, it explores generalization by subtracting a reward baseline and deriving an online gradient rule, showcasing its effectiveness in maximizing rewards.