Reinforcement Learning in 3 Hours | Full Course using Python

Want to get started with Reinforcement Learning?

This is the course for you!

This course will take you through all of the fundamentals required to get started with reinforcement learning with Python, OpenAI Gym and Stable Baselines. You'll be able to build deep learning powered agents to solve a varying number of RL problems including CartPole, Breakout and CarRacing as well as learning how to build your very own environment!

In this video you'll learn:
1. All the basics to get up and started with Reinforcement Learning
2. How to build custom environments using OpenAI Gym
3. About working on custom projects for Reinforcement Learning

Get the code for this tutorial: https://github.com/nicknochnack/ReinforcementLearningCourse

Links Mentioned
Stable Baselines 3: https://stable-baselines3.readthedocs.io/en/master/
OpenAI Gym: https://gym.openai.com/
PyTorch: https://pytorch.org/
Atarimania ROMs: http://www.atarimania.com/roms/Roms.rar
Swig: http://www.swig.org/Doc1.3/Windows.html

Chapters
0:00 - Start
0:23 - Introduction
1:15 - Gameplan
4:24 - RL in a Nutshell
13:30 - 1. Setup Stable Baselines
21:45 - 2. Environments
30:10 - Loading OpenAI Gym Environments
40:00 - Understanding OpenAI Gym Environments
42:58 - 3. Training
51:32 - Train a Reinforcement Learning Model
1:00:00 - Saving and Reloading Environments
1:04:23 - 4. Testing and Evaluation
1:06:35 - Evaluating RL Models
1:09:34 - Testing the Agent
1:15:56 - Viewing Logs in Tensorboard
1:24:50 - Performance Tuning
1:26:31 - 5. Callbacks, Alternate Algorithms, Neural Networks
1:27:39 - Adding Training Callbacks
1:34:44 - Changing Policies
1:38:27 - Changing Algorithms
1:40:29 - 6. Projects
1:41:31 - Project 1 Atari
1:41:51 - Importing Dependencies
1:44:16 - Applying GPU Acceleration with PyTorch
1:45:11 - Testing Atari Environments
1:51:35 - Vectorizing Environments
1:56:48 - Save and Reload Atari Model
1:57:45 - Evaluate and Test Atari RL Model
2:02:16 - Updated Performance
2:06:34 - Project 2 Autonomous Driving
2:06:56 - Installing Dependencies
2:09:27 - Test CarRacing-v0 Environment
2:12:23 - Train Autonomous Driving Agent
2:17:16 - Save and Reload Self Driving model
2:18:20 - Updated Self Driving Performance
2:28:56 - Project 3 Custom Open AI Gym Environments
2:29:35 - Import Dependencies for Custom Environment
2:32:00 - Types of OpenAI Gym Spaces
2:38:47 - Building a Custom Open AI Environment
2:51:49 - Testing a Custom Environment
2:52:49 - Train a RL Model for a Custom Environment
2:56:22 - Save a Custom Environment Model
2:58:49 - 7. Wrap Up

Oh, and don't forget to connect with me!
LinkedIn: https://bit.ly/324Epgo
Facebook: https://bit.ly/3mB1sZD
GitHub: https://bit.ly/3mDJllD
Patreon: https://bit.ly/2OCn3UW
Join the Discussion on Discord: https://bit.ly/3dQiZsV

Happy coding!
Nick

P.s. Let me know how you go and drop a comment if you need a hand!