Deep RL Course documentation

Conclusion

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

Introduction PPO with Sample Factory and Doom Conclusion

Bonus Unit 3. Advanced Topics in Reinforcement Learning

Bonus Unit 5. Imitation Learning with Godot RL Agents

Certification and congratulations

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Collaborate on models, datasets and Spaces

Faster examples with accelerated inference

Switch between documentation themes

to get started

Conclusion

That’s all for today. Congrats on finishing this Unit and the tutorial! ⭐️

Now that you’ve successfully trained your Doom agent, why not try deathmatch? Remember, that’s a much more complex level than the one you’ve just trained, but it’s a nice experiment and I advise you to try it.

If you do it, don’t hesitate to share your model in the #rl-i-made-this channel in our discord server.

This concludes the last unit, but we are not finished yet! 🤗 The following bonus unit includes some of the most interesting, advanced, and cutting edge work in Deep Reinforcement Learning.

See you next time 🔥

Keep Learning, Stay awesome 🤗

< > Update on GitHub

←PPO with Sample Factory and Doom Introduction→