Deep RL Course documentation
Conclusion
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
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
IntroductionAn introduction to Multi-Agents Reinforcement Learning (MARL)Designing Multi-Agents systemsSelf-PlayLet's train our soccer team to beat your classmates' teams (AI vs. AI)QuizConclusionAdditional Readings
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
Conclusion
That’s all for today. Congrats on finishing this unit and the tutorial!
The best way to learn is to practice and try stuff. Why not train another agent with a different configuration?
And don’t hesitate from time to time to check the leaderboard
See you in Unit 8 🔥