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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.
| Date | Stars |
|---|---|
| 2026-07-24 | 2844 |
| 2026-07-25 | 2845 |
| 2026-07-28 | 2845 |
| 2026-07-30 | 2845 |
| 2026-08-06 | 2845 |
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-929292)   [](https://github.com/psf/black)  [](https://discord.gg/GB2vwsF)  # MuZero General A commented and [documented](https://github.com/werner-duvaud/muzero-general/wiki/MuZero-Documentation) implementation of MuZero based on the Google DeepMind [paper](https://arxiv.org/abs/1911.08265) (Schrittwieser et al., Nov 2019) and the associated [pseudocode](https://arxiv.org/src/1911.08265v2/anc/pseudocode.py). It is designed to be easily adaptable for every games or reinforcement learning environments (like [gym](https://github.com/openai/gym)). You only need to add a [game file](https://github.com/werner-duvaud/muzero-general/tree/master/games) with the hyperparameters and the game class. Please refer to the [documentation](https://github.com/werner-duvaud/muzero-general/wiki/MuZero-Documentation) and the [example](https://github.com/werner-duvaud/muzero-general/blob/master/games/cartpole.py). This implementation is primarily for educational purpose.\ [Explanatory video of MuZero](https://youtu.be/We20YSAJZSE) MuZero is a state of the art RL algorithm for board games (Chess, Go, ...) and Atari games. It is the successor to [AlphaZero](https://arxiv.org/abs/1712.01815) but without any knowledge of the environment underlying dynamics. MuZero learns a model of the environment and uses an internal representation that contains only the useful information for predicting the reward, value, policy and transitions. MuZero is also close to [Value prediction networks](https://arxiv.org/abs/1707.03497). See [How it works](https://github.com/werner-duvaud/muzero-general/wiki/How-MuZero-works). ## Features * [x] Residual Network and Fully connected network in [PyTorch](https://github.com/pytorch/pytorch) * [x] Multi-Threaded/Asynchronous/[Cluster](https://docs.ray.io/en/latest/cluster-index.html) with [Ray](https://github.com/ray-project/ray) * [X] Multi GPU support for the training and the selfplay * [x] TensorBoard real-time monitoring * [x] Model weights automatically saved at checkpoints * [x] Single and two player mode * [x] Commented and [documented](https://github.com/werner-duvaud/muzero-general/wiki/MuZero-Documentation) * [x] Easily adaptable for new games * [x] [Examples](https://github.com/werner-duvaud/muzero-general/blob/master/games/cartpole.py) of board games, Gym and Atari games (See [list of implemented games](https://github.com/werner-duvaud/muzero-general#games-already-implemented)) * [x] [Pretrained weights](https://github.com/werner-duvaud/muzero-general/tree/master/results) available * [ ] Windows support (Experimental / Workaround: Use the [notebook](https://github.com/werner-duvaud/muzero-general/blob/master/notebook.ipynb) in [Google Colab](https://colab.research.google.com)) ### Further improvements Here is a list of features which could be interesting to add but which are not in MuZero's paper. We are open to contributions and other ideas. * [x] [Hyperparameter search](https://github.com/werner-duvaud/muzero-general/wiki/Hyperparameter-Optimization) * [x] [Continuous action space](https://github.com/werner-duvaud/muzero-general/tree/continuous) * [x] [Tool to understand the learned model](https://github.com/werner-duvaud/muzero-general/blob/master/diagnose_model.py) * [ ] Batch MCTS * [ ] Support of more than two player games ## Demo All performances are tracked and displayed in real time in [TensorBoard](https://www.tensorflow
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Wei Zhang
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tom white · New Zealand
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Luke Wood
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LazyNeuron · University of Alberta · Canada
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Fred Drake
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Sergio Vieri
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Adam Lawson · United Kingdom
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:6dd5ad078bacf1bf, topic:reinforcement-learning, topic:deep-reinforcement-learning, topic:rl
matched fp:6dd5ad078bacf1bf, topic:deep-learning, topic:neural-network, topic:pytorch
