Deep RL Course documentation

Interesting Environments to try

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

Introduction Model-Based Reinforcement Learning Offline vs. Online Reinforcement Learning Reinforcement Learning from Human Feedback Decision Transformers and Offline RL Language models in RL (Automatic) Curriculum Learning for RL Interesting environments to try An introduction to Unreal Learning Agents An Introduction to Godot RL Brief introduction to RL documentation

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Interesting Environments to try

Here we provide a list of interesting environments you can try to train your agents on:

MineRL

MineRL

MineRL is a Python library that provides a Gym interface for interacting with the video game Minecraft, accompanied by datasets of human gameplay. Every year there are challenges with this library. Check the website

To start using this environment, check these resources:

DonkeyCar Simulator

Donkey Car

Donkey is a Self Driving Car Platform for hobby remote control cars. This simulator version is built on the Unity game platform. It uses their internal physics and graphics and connects to a donkey Python process to use our trained model to control the simulated Donkey (car).

To start using this environment, check these resources:

Starcraft II

Alphastar

Starcraft II is a famous real-time strategy game. DeepMind has used this game for their Deep Reinforcement Learning research with Alphastar

To start using this environment, check these resources:

Author

This section was written by Thomas Simonini

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