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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.
Deep-Reinforcement-Learning-Hands-On-Second-Edition, published by Packt
| Date | Stars |
|---|---|
| 2026-07-31 | 1288 |
| 2026-08-06 | 1288 |
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# Deep-Reinforcement-Learning-Hands-On-Second-Edition Deep-Reinforcement-Learning-Hands-On-Second-Edition, published by Packt ## Code branches The repository is maintained to keep dependency versions up-to-date. This requires efforts and time to test all the examples on new versions, so, be patient. The logic is following: there are several branches of the code, corresponding to major pytorch version code was tested. Due to incompatibilities in pytorch and other components, **code in the printed book might differ from the code in the repo**. At the moment, there are the following branches available: * `master`: contains the code with the latest pytorch which was tested. At the moment, it is pytorch 1.7. * `torch-1.3-book`: code printed in the book with minor bug fixes. Uses pytorch=1.3 which is available only on conda repos. * `torch-1.7`: pytorch 1.7. This branch was tested and merged into master. All the branches uses python 3.7, more recent versions weren't tested. ## Dependencies installation Anaconda is recommended for virtual environment creation. Once installed, the following steps will install everything needed: * change directory to book repository dir: `cd Deep-Reinforcement-Learning-Hands-On-Second-Edition` * create virtual environment with `conda create -n rlbook python=3.7` * activate it: `conda activate rlbook` * install pytorch (update CUDA version according to your CUDA): `conda install pytorch==1.7 torchvision torchaudio cudatoolkit=10.2 -c pytorch` * install rest of dependencies: `pip install requirements.txt` Now you're ready to launch and experiment with examples!
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Max Lapan · Exasol · Germany
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:9eb4a1f62aa79dae, name:reinforcement learning, desc:reinforcement learning