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

Introduction Optuna Hands-on

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

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Introduction

One of the most critical tasks in Deep Reinforcement Learning is to find a good set of training hyperparameters.

Optuna is a library that helps you to automate the search. In this Unit, we’ll study a little bit of the theory behind automatic hyperparameter tuning. We’ll first try to optimize the parameters of the DQN studied in the last unit manually. We’ll then learn how to automate the search using Optuna.

Update on GitHub

←Additional Readings Optuna→