Book Content
chapters • 11h20m total length
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
