Deep RL Course documentation
Additional Readings
Unit 0. Welcome to the course
Unit 1. Introduction to Deep Reinforcement Learning
Bonus Unit 1. Introduction to Deep Reinforcement Learning with Huggy
Live 1. How the course work, Q&A, and playing with Huggy
Unit 2. Introduction to Q-Learning
Unit 3. Deep Q-Learning with Atari Games
Bonus Unit 2. Automatic Hyperparameter Tuning with Optuna
Unit 4. Policy Gradient with PyTorch
IntroductionWhat are the policy-based methods?The advantages and disadvantages of policy-gradient methodsDiving deeper into policy-gradient(Optional) the Policy Gradient TheoremGlossaryHands-onQuizConclusionAdditional Readings
Unit 5. Introduction to Unity ML-Agents
Unit 6. Actor Critic methods with Robotics environments
Unit 7. Introduction to Multi-Agents and AI vs AI
Unit 8. Part 1 Proximal Policy Optimization (PPO)
Unit 8. Part 2 Proximal Policy Optimization (PPO) with Doom
Bonus Unit 3. Advanced Topics in Reinforcement Learning
Bonus Unit 5. Imitation Learning with Godot RL Agents
Certification and congratulations
Additional Readings
These are optional readings if you want to go deeper.
Introduction to Policy Optimization
Policy Gradient
- https://johnwlambert.github.io/policy-gradients/
- RL - Policy Gradient Explained
- Chapter 13, Policy Gradient Methods; Reinforcement Learning, an introduction by Richard Sutton and Andrew G. Barto