AdamStelmaszczyk/rl-tutorial
Source code for "A deep dive into reinforcement learning"
This project offers a practical demonstration of reinforcement learning using the Mountain Car problem. It takes raw environment observations and outputs a trained model capable of solving the task, alongside visualizations like GIFs showing the model in action. This is for machine learning researchers or students who want to understand and implement Deep Q-Networks (DQN) for control tasks.
No commits in the last 6 months.
Use this if you are studying or experimenting with reinforcement learning and want to see a concrete, working example of DQN applied to a classic control problem.
Not ideal if you need a production-ready solution for complex, real-world control systems or are looking for a high-level API without diving into the implementation details.
Stars
13
Forks
6
Language
Python
License
MIT
Category
Last pushed
Dec 17, 2019
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/AdamStelmaszczyk/rl-tutorial"
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