This repository provides Jupyter Notebook implementations of popular reinforcement learning algorithms including dynamic programming, Monte Carlo methods, temporal difference learning, function approximation, deep Q learning, and policy gradient methods. Code uses OpenAI Gym for environment simulation and Tensorflow for neural network implementations. Each section includes learning goals, concept summaries, and solutions to textbook exercises.