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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Rainbow is all you need! A step-by-step tutorial from DQN to Rainbow
| Date | Stars |
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| 2026-07-24 | 2027 |
| 2026-07-25 | 2027 |
| 2026-07-28 | 2027 |
| 2026-07-30 | 2027 |
| 2026-08-06 | 2027 |
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<div align="center"> <img src="assets/banner.svg" alt="Rainbow is All You Need" width="800"/> [](#contributors) </div> This is a step-by-step tutorial from DQN to Rainbow. Every chapter contains both of theoretical backgrounds and object-oriented implementation. Just pick any topic in which you are interested, and learn! You can run them directly in the cloud with [molab](https://molab.marimo.io/) — no local setup needed. Built with [marimo](https://marimo.io/) — a reactive Python notebook that runs as a pure `.py` file with better reproducibility, git diffs, and interactive UI. Please feel free to open an issue or a pull-request if you have any idea to make it better. :) >If you want a tutorial for policy gradient methods, please see [PG is All You Need](https://github.com/MrSyee/pg-is-all-you-need). ## Contents 01. DQN [[GitHub](01_dqn.py)] [[Preview](https://molab.marimo.io/github/Curt-Park/rainbow-is-all-you-need/blob/master/01_dqn.py)] 02. DoubleDQN [[GitHub](02_double_q.py)] [[Preview](https://molab.marimo.io/github/Curt-Park/rainbow-is-all-you-need/blob/master/02_double_q.py)] 03. PrioritizedExperienceReplay [[GitHub](03_per.py)] [[Preview](https://molab.marimo.io/github/Curt-Park/rainbow-is-all-you-need/blob/master/03_per.py)] 04. DuelingNet [[GitHub](04_dueling.py)] [[Preview](https://molab.marimo.io/github/Curt-Park/rainbow-is-all-you-need/blob/master/04_dueling.py)] 05. NoisyNet [[GitHub](05_noisy_net.py)] [[Preview](https://molab.marimo.io/github/Curt-Park/rainbow-is-all-you-need/blob/master/05_noisy_net.py)] 06. CategoricalDQN [[GitHub](06_categorical_dqn.py)] [[Preview](https://molab.marimo.io/github/Curt-Park/rainbow-is-all-you-need/blob/master/06_categorical_dqn.py)] 07. N-stepLearning [[GitHub](07_n_step_learning.py)] [[Preview](https://molab.marimo.io/github/Curt-Park/rainbow-is-all-you-need/blob/master/07_n_step_learning.py)] 08. Rainbow [[GitHub](08_rainbow.py)] [[Preview](https://molab.marimo.io/github/Curt-Park/rainbow-is-all-you-need/blob/master/08_rainbow.py)] 09. Rainbow IQN [[GitHub](09_rainbow_iqn.py)] [[Preview](https://molab.marimo.io/github/Curt-Park/rainbow-is-all-you-need/blob/master/09_rainbow_iqn.py)] > Click **"Run in molab"** on the preview page to open an interactive session where you can edit and run the notebook. ## Prerequisites ```bash # Install mise curl https://mise.run | sh # Clone the project git clone https://github.com/Curt-Park/rainbow-is-all-you-need.git cd rainbow-is-all-you-need # Install Python + Create venv + Install Python packages make init make setup ``` ## How to Run Run and experiment with any notebook: ``` make run notebook=01_dqn.py ``` ## Development ``` make format # run the formatter make lint # run the linter ``` ## Related Papers 01. [V. Mnih et al., "Human-level control through deep reinforcement learning." Nature, 518 (7540):529–533, 2015.](https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf) 02. [van Hasselt et al., "Deep Reinforcement Learning with Double Q-learning." arXiv preprint arXiv:1509.06461, 2015.](https://arxiv.org/pdf/1509.06461.pdf) 03. [T. Schaul et al., "Prioritized Experience Replay." arXiv preprint arXiv:1511.05952, 2015.](https://arxiv.org/pdf/1511.05952.pdf) 04. [Z. Wang et al., "Dueling Network Architectures for Deep Reinforcement Learning." arXiv preprint arXiv:1511.06581, 2015.](https://arxiv.org/pdf/1511.06581.pdf) 05. [M. Fortunato et al., "Noisy Networks for Exploration." arXiv preprint arXiv:1706.10295, 2017.](https://arxiv.org/pdf/1706.10295.pdf) 06. [M. G. Bellemare et al., "A Distributional Perspective on Reinforcement Learning." arXiv preprint arXiv:1707.06887, 2017.](https://arxiv.org/pdf/1707.06887.pdf) 07. [R. S. Sutton, "Learning to predict by the methods of temporal differences." Machine learning, 3(1):9–44, 1988.](http://incompleteideas.net/papers/sutton-88-with-erratum.pdf) 08. [M. He
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WANG Lei · China
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Roberto Schiavone · Netherlands
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:dba46f7b0eeb0175, topic:reinforcement-learning, readme:reinforcement learning, readme:policy gradient
matched fp:dba46f7b0eeb0175, topic:pytorch