This is the Syllabus for Siraj Raval's Reinforcement Learning course "AI for Video Games" on Youtube
Each week has listed first a shorter, introductory video (released on Friday) and a longer, in-depth video (released on Wednesday)
- Markov Decision Processes
- Policy evaluation, iteration, value iteration
- Monte Carlo Prediction
- Monte Carlo with Epsilon-Greedy Policies and off-policy control with importance sampling
- Q Learning
- Deep Q Learning
- Policy Gradients
- Actor Critic
- TRPO
- PPO
- Deep RL experimentation
- Stochastic Computation Graphs + SVG + DDP
- Derivative Free methods
- HyperNEAT
- Model based RL
- Inverse RL
- Imitation learning
- Open Problems
- Future Directions for RL
- A technique i'll talk about that hasn't even been created yet when i made this syllabus because this field moves fast AF