YuvrajSingh-mist/NeatRL
Repository of implementations of classic and sota rl algorithms from scratch in PyTorch
This helps AI researchers and students rapidly experiment with different reinforcement learning (RL) algorithms for training autonomous agents. You input an environment (like a game or a simulation) and a chosen RL algorithm, and it outputs a trained agent capable of performing tasks within that environment, along with visualizations of its learning process. This is for machine learning practitioners focused on developing and evaluating intelligent agent behavior.
221 stars.
Use this if you need to quickly train and compare various reinforcement learning agents in simulated environments like games or physics simulations.
Not ideal if you're looking for a low-code solution for deploying agents in complex real-world systems without prior RL expertise.
Stars
221
Forks
21
Language
Python
License
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Category
Last pushed
Jan 03, 2026
Commits (30d)
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