Deep RL Course documentation

Additional Readings

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

Introduction The Problem of Variance in Reinforce Advantage Actor Critic (A2C) Advantage Actor Critic (A2C) using Robotics Simulations with Panda-Gym 🤖 Quiz Conclusion Additional Readings

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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Additional Readings

Bias-variance tradeoff in Reinforcement Learning

If you want to dive deeper into the question of variance and bias tradeoff in Deep Reinforcement Learning, you can check out these two articles:

Advantage Functions

Advantage Functions, SpinningUp RL

Actor Critic

< > Update on GitHub

←Conclusion Introduction→