Implement classic papers from scratch
Pick any paper below and PaperNova generates a guided workbook: an outline, exercise-wise explanations from beginner to advanced, and a downloadable Jupyter notebook you can run locally.
Proximal Policy Optimization Algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, +2 more
PPO. A clipped-surrogate policy-gradient method that balances stability and simplicity — the default RL algorithm behind RLHF and most modern agents.
Mastering the Game of Go with Deep Neural Networks and Tree Search
David Silver, Aja Huang, Chris J. Maddison, +1 more
AlphaGo. Combines deep policy/value networks with Monte Carlo tree search to beat the world champion — a landmark demonstration of RL at scale.
Playing Atari with Deep Reinforcement Learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, +2 more
DQN. Deep Q-Networks learn to play Atari games from raw pixels, kickstarting the deep reinforcement learning era.
Why implement classic papers?
Reading a paper and implementing it are two very different skills. PaperNova's workbook tool bridges that gap: Gemini turns the paper into a sequence of small, self-contained exercises — from a warm-up reimplementation of the core idea up to advanced extensions — then assembles them into a Jupyter notebook you can run, edit and extend.
Prefer to work from your own paper? Upload a PDF and get the same guided workbook tailored to it.