FareedKhan-dev/all-rl-algorithms
Implementation of all RL algorithms in a simpler way
This collection of Jupyter Notebooks helps AI/ML practitioners understand how Reinforcement Learning (RL) algorithms work from the ground up. Each notebook provides a step-by-step, plain-language explanation and Python code for a specific RL algorithm. The output is a clear, executable demonstration of concepts like Q-learning, PPO, and DQN, which an AI/ML developer can then apply to their own projects.
1,408 stars. No commits in the last 6 months.
Use this if you are an AI/ML developer or researcher who wants to deeply understand the mechanics of various Reinforcement Learning algorithms without the abstraction of complex libraries.
Not ideal if you need a high-performance, production-ready RL library or if you are looking for advanced features beyond fundamental algorithm implementations.
Stars
1,408
Forks
248
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Aug 29, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/llm-tools/FareedKhan-dev/all-rl-algorithms"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related tools
inclusionAI/AReaL
Lightning-Fast RL for LLM Reasoning and Agents. Made Simple & Flexible.
melih-unsal/DemoGPT
🤖 Everything you need to create an LLM Agent—tools, prompts, frameworks, and models—all in one place.
AOSSIE-Org/Perspective
Perspective analyzes your news or social feed and presents credible counter-narratives from...
expectedparrot/edsl
Design, conduct and analyze results of AI-powered surveys and experiments. Simulate social...
kaushikb11/awesome-llm-agents
A curated list of awesome LLM agents frameworks.