brianspiering/rl-course
Applied Reinforcement Learning course
This course teaches you how to use Reinforcement Learning (RL) techniques to solve real-world problems. You'll learn the core concepts and then apply them to design systems where agents learn optimal actions within an environment, enabling solutions for areas like video games, robotics, or improving AI models with human feedback. This is for individuals looking to apply advanced AI to create intelligent, self-learning systems.
No commits in the last 6 months.
Use this if you have a strong background in machine learning and Python, and you want to build systems that learn optimal decision-making strategies in dynamic environments.
Not ideal if you're new to machine learning, probability, or Python, as the course requires existing foundational knowledge.
Stars
12
Forks
1
Language
Jupyter Notebook
License
—
Category
Last pushed
Feb 14, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/brianspiering/rl-course"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
DLR-RM/stable-baselines3
PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
google-deepmind/dm_control
Google DeepMind's software stack for physics-based simulation and Reinforcement Learning...
Denys88/rl_games
RL implementations
pytorch/rl
A modular, primitive-first, python-first PyTorch library for Reinforcement Learning.
yandexdataschool/Practical_RL
A course in reinforcement learning in the wild