Pulkit-Khandelwal/Reinforcement-Learning-Notebooks resource

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1045 stars · 195 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of Jupyter Notebook implementations of reinforcement learning algorithms from Sutton and Barto's book and related research papers, written in Python. It was created as study notes for a graduate RL course at McGill and is intended to accompany the book and David Silver's lecture videos.

Use cases

• learn reinforcement learning algorithms by reading runnable implementations
• study Sutton and Barto's book with companion code
• implement classic RL algorithms like Q-learning and policy gradients from papers
• supplement David Silver's RL course videos with hands-on notebooks
• reference implementations while taking a reinforcement learning course

When to choose

• you are learning RL from Sutton and Barto's book and want code to follow along
• you want simple, readable notebook-style implementations of classic RL algorithms
• you are taking a course on reinforcement learning and need study references

When to avoid

• you need production-ready or well-engineered RL code
• you want a maintained library with an API, tests, or recent updates
• you need modern deep RL frameworks like stable-baselines or RLlib

Facets

learning-resource · maturity abandoned

reinforcement-learning machine-learning developer-tools reinforcement-learning machine-learning tutorials education python cross-platform jupyter-notebooks sutton-barto educational algorithms study-notes

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markdown · JSON · MCP: product_card(name="Pulkit-Khandelwal/Reinforcement-Learning-Notebooks")