Deep RL Course documentation
Additional Readings
Unit 0. Welcome to the course
Unit 1. Introduction to Deep Reinforcement Learning
IntroductionWhat is Reinforcement Learning?The Reinforcement Learning FrameworkThe type of tasksThe Exploration/ Exploitation tradeoffThe two main approaches for solving RL problemsThe “Deep” in Deep Reinforcement LearningSummaryGlossaryHands-onQuizConclusionAdditional Readings
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
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.
Deep Reinforcement Learning
- Reinforcement Learning: An Introduction, Richard Sutton and Andrew G. Barto Chapter 1, 2 and 3
- Foundations of Deep RL Series, L1 MDPs, Exact Solution Methods, Max-ent RL by Pieter Abbeel
- Spinning Up RL by OpenAI Part 1: Key concepts of RL