PyTorch 1.x Reinforcement Learning Cookbook

Table of Contents

1. Getting started with reinforcement learning and PyTorch
2. Markov Decision Process and Dynamic Programming
3. Monte Carlo Methods for making numerical estimations
4. Temporal Difference and Q-Learning
5. Solving Multi Armed Bandit problems
6. Scaling up Learning with Function Approximation
7. Deep Q-Networks in Action
8. Implementing Policy Gradients and Policy Optimization
9. Capstone Project: Playing Flappy Bird with DQN