Code from the Deep Reinforcement Learning in Action book from Manning, Inc.
DeepReinforcementLearning/DeepReinforcementLearningInAction is ranked 13,212 with 851 stars.
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Key parameters and specifications
Key parameters and specifications| Parameter | Value |
|---|
| Language | Jupyter Notebook |
|---|
| Category | Tutorials |
|---|
| Subcategory | ML/AI fundamentals |
|---|
| Keywords | RL |
|---|
| Stars | 851 |
|---|
| Forks | 350 |
|---|
| Contributors | 10 |
|---|
| Created date | 2018-03-27 |
|---|
| Updated date | 2026-10-15 |
|---|
| Stars in 1 day | 0 |
|---|
| Stars in 7 days | 0 |
|---|
| Rank | 13,212 |
|---|
| Previous rank | 18,891 |
|---|
| Rank change | 5,679 |
|---|
| 7-day stars rank | 13,819 |
|---|
| Previous 7-day stars rank | 13,835 |
|---|
| 7-day stars rank change | 16 |
|---|
| Total stars rank | 12,686 |
|---|
| Previous total stars rank | 12,673 |
|---|
| Total stars rank change | -13 |
|---|
README summary
Deep Reinforcement Learning In Action Code Snippets from the Deep Reinforcement Learning in Action book from Manning, Inc How this is Organized The code snippets, listings, and projects are all embedded in Jupyter Notebooks organized by chapter. Visit for instructions on installing Jupyter Notebooks. The chapter directories contain the maintained notebooks. See Book compatibility and corrections for API updates and corrections to printed listings, and Errata for links to maintained chapters and the immutable pre-refresh source. Model architectures and
the methods described in the text are preserved; stochastic learning results vary between runs. Requirements In order to run many of the projects, you'll need at least the NumPy library and PyTorch. 13 environment and launch each notebook from its chapter directory. See validation results for full training outcomes and tested limits. Contribute If you experience any issues running the examples, please file an issue. If you see typos or other errors in the book, please edit the Errata. md
for PR regression/smoke checks, weekly and manual full-training runs, local commands, and merge-protection setup.
Frequently asked questions
How many stars and forks are recorded?
DeepReinforcementLearning/DeepReinforcementLearningInAction has 851 stars and 350 forks.
What is the repository rank?
DeepReinforcementLearning/DeepReinforcementLearningInAction is ranked 13,212.
What language and category are recorded?
DeepReinforcementLearning/DeepReinforcementLearningInAction has 851 stars, uses Jupyter Notebook and belongs to Tutorials.
Which repository is closest in rank?
DeepReinforcementLearning/DeepReinforcementLearningInAction is compared with openai/build-hours, ranked 13,207 with 852 stars.
Deep Reinforcement Learning In Action
Code Snippets from the Deep Reinforcement Learning in Action book from Manning, Inc
How this is Organized
The code snippets, listings, and projects are all embedded in Jupyter Notebooks
organized by chapter. Visit http://jupyter.org/install for
instructions on installing Jupyter Notebooks.
The chapter directories contain the maintained notebooks. See Book compatibility and corrections for API updates and corrections to printed listings, and Errata for links to maintained chapters and the immutable pre-refresh source. Model architectures and the methods described in the text are preserved; stochastic learning results vary between runs.
Requirements
In order to run many of the projects, you'll need at least the NumPy library
and PyTorch.
python -m pip install -r requirements.txt
Use a fresh Python 3.13 environment and launch each notebook from its chapter directory. Run python -m unittest discover -s tests -v for the fast checks. See validation results for full training outcomes and tested limits.
Contribute
If you experience any issues running the examples, please file an issue.
If you see typos or other errors in the book, please edit the Errata.md file and create a pull request.
Automated tests
See TESTING.md for PR regression/smoke checks, weekly and manual
full-training runs, local commands, and merge-protection setup.