Book Content
chapters • 10h36m total length
1. Introduction to Reinforcement Learning
2. Getting started with OpenAI and Tensorflow
3. Markov Decision process and Dynamic Programming
4. Gaming with Monte Carlo Tree Search
5. Temporal Difference Learning
6. Multi-Armed Bandit Problem
7. Deep Learning Fundamentals
8. Deep Learning and Reinforcement
9. Playing Doom With Deep Recurrent Q Network
10. Asynchronous Advantage Actor Critic Network
11. Policy Gradients and Optimization
12. Capstone Project – Car Racing using DQN
13. Current Research and Next Steps
