TheoLvs/reinforcement-learning
Personal experiments on Reinforcement Learning
This project explores how to teach systems to make optimal decisions through trial and error, much like humans learn from experience. It takes data from simulations, games, or real-world environments and produces intelligent agents or control policies that can perform complex tasks autonomously. This is for researchers, engineers, or students interested in building self-learning systems for various applications.
119 stars. No commits in the last 6 months.
Use this if you want to understand and apply different techniques to build intelligent agents that can learn to optimize actions in environments like robotics, game playing, or resource management.
Not ideal if you need a plug-and-play solution for a specific business problem without needing to understand the underlying machine learning algorithms.
Stars
119
Forks
50
Language
Jupyter Notebook
License
—
Category
Last pushed
Apr 29, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/TheoLvs/reinforcement-learning"
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