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
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
Additional Readings
An introduction to multi-agents
- Multi-agent reinforcement learning: An overview
- Multiagent Reinforcement Learning, Marc Lanctot
- Example of a multi-agent environment
- A list of different multi-agent environments
- Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents
- Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning
Self-Play and MA-POCA
- Self Play Theory and with MLAgents
- Training complex behavior with MLAgents
- MLAgents plays dodgeball
- On the Use and Misuse of Absorbing States in Multi-agent Reinforcement Learning (MA-POCA)