Reinforce Algorithm Explained: The Foundation of Reinforcement Learning 🧠

If you would like to see more videos like this please consider supporting me on Patreon -https://www.patreon.com/andriydrozdyuk Reinforcement Learning: An Introduction, 2nd Ed, Sutton & Barto For REINFORCE algorithm see Section "13.3 REINFORCE: Monte Carlo Policy Gradient": http://incompleteideas.net/book/the-book-2nd.html Complete code used in the video can be found here: https://github.com/drozzy/reinforce 0:00 - Introduction 0:15 - Intro to RL 0:38 - Problem with Environment 1:02 - Why is this a problem for RL? 1:41 - Puppy treats (low level of abstraction) 2:14 - Good actions (middle level of abstraction) 3:22 - Reward as a signal (high level of abstraction) 4:04 - REINFORCE Algorithm Overview 5:11 - Collected Trajectory 6:01 - Product of G and Policy Gradient 6:34 - Two key concepts: sample and evaluate 6:48 - Sampling an action 7:22 - Sampling in REINFORCE 7:38 - Evaluating an action 8:24 - Sampling vs. Evaluating 8:41 - Sampling using torch.distributions.Categorical 9:12 - Evaluating using torch.distributions.Categorical 9:50 - Env/NN/Optim 10:07 - Collect One Episode of Experience 10:53 - Compute Discounted Returns 11:44 - Update the Policy 12:41 - Executing Trained Policy 13:04 - Demo Cart Pole Balancing