https://github.com/achronus/rl-hub
The ultimate resource for journeying through the incredible world of Reinforcement Learning 🤖
https://github.com/achronus/rl-hub
documentation examples-python machine-learning pytorch reinforcement-learning
Last synced: 9 months ago
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The ultimate resource for journeying through the incredible world of Reinforcement Learning 🤖
- Host: GitHub
- URL: https://github.com/achronus/rl-hub
- Owner: Achronus
- License: mit
- Created: 2024-09-26T19:58:23.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-03T11:45:15.000Z (about 1 year ago)
- Last Synced: 2025-01-21T14:13:24.125Z (11 months ago)
- Topics: documentation, examples-python, machine-learning, pytorch, reinforcement-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 1.19 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Reinforcement Learning Hub
*Learning* — a fundamental skill that we use daily to shape our lives. Driven by the mistakes we make, the feedback we receive, and the adjustments we make along the way. It's the superpower we all have, but never dreamed of and one of the keys to helping us *generalise* to different scenarios.
What if I told you a Machine can do the same thing? That it could learn, adapt, and improve through experience just like we do, unlocking the potential to solve complex problems by simply learning by doing?
Well, today we can! And some of the patterns and techniques these machines can learn are mind-blowing.
How about [beating a world champion](https://deepmind.google/technologies/alphago/) at the board game Go? Or, becoming so good at [hide and seek](https://openai.com/index/emergent-tool-use/) that you break the rules originally intended for the game?
Welcome to the incredible world of Reinforcement Learning 🤖.
## What Is The RL Hub?
RL Hub is an ambitious project that intends to take you through a journey of Reinforcement Learning, helping you to grasp its core concepts and really gain a deep intuition on the subject.
Here, you'll explore more than just its theory and algorithms. You'll also learn the mathematical foundations to help you understand how and why it works the way it does.
Our goal is to unshroud the mystery and thinking behind building powerful algorithms that shape the future of Machine Intelligence, and hopefully, give you the tools you need to better our world.
You can expect:
- **Interactive Tutorials**: engaging with hands-on exercises illustrating key concepts, from turning math to code or building an algorithm from scratch.
- **Case Studies**: Deep dives into real-world applications of RL and how they have been used in leading companies.
- **Community**: a community of like-minded individuals to connect with, share insights, and discuss challenges and ideas that could be the next big groundbreaking research.
## Why Reinforcement Learning?
In the *Machine Learning* landscape, RL stands out for its unique ability to tackle problems without a clearly defined path to success. Unlike *Supervised Learning*, where models are trained on labelled datasets, RL agents learn from the consequences of their own actions in an environment - as we do!
They receive rewards or penalties based on their choices, allowing them to discover optimal strategies over time.
This ability makes RL incredibly powerful for a variety of applications, including:
- **Robotics**: teaching robots how to walk, grab items, and navigate through their environment.
- **Autonomous Systems**: developing complex systems such as self-driving cars, planes, or even optimising power usage in reactors.
- **Healthcare** - creating personalised treatment plans and optimizing drug discovery processes.