Textbook: Sutton and Barton: Reinforcement Learning An Inttroduction second edition
- Markov decision process
- Policy iteration and value iteration
- Model-free prediction
- Model-free control
- On-Policy learning and off-policy learning
- Connection to optimal control
- Value function approximation
- Deep Q Learning
- Policy optimization
- Imitation learning
- Model-based Reinforcement Learning
- Exploration and exploitation
- Distributed computing and RL system design
- Inverse RL and Real-world RL