Deep RL Course documentation

Introduction:

Deep RL Course

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

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

Introduction The environment Getting started Train our robot (Optional) Customize the environment Conclusion

Certification and congratulations

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Introduction:

Unit bonus 4 thumbnail

Welcome to this bonus unit, where you will train a robot agent to complete a mini-game level using imitation learning.

At the end of the unit, you will have a trained agent capable of solving the level as in the video:

Objectives:

Learn how to use imitation learning with Godot RL Agents by training an agent to complete a mini-game environment using human-recorded expert demonstrations.

Prerequisites and requirements:

It is recommended that you complete the previous chapter (Godot RL Agents) before starting this tutorial,
Some familiarity with Godot is recommended, although completing the tutorial does not require any gdscript coding knowledge,
Godot with .NET support (tested to work with 4.3.dev5 .NET, may work with newer versions too),
Godot RL Agents (you can use pip install godot-rl in the venv/conda env),
Imitation library,
Time: ~1-2 hours to complete the project and training. It can be outside of this range depending on the hardware used.

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