deep-rl | Collection of Deep Reinforcement Learning algorithms | Machine Learning library

by pemami4911 Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | deep-rl Summary

deep-rl is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. deep-rl has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However deep-rl build file is not available. You can download it from GitHub.

Collection of Deep Reinforcement Learning algorithms

Support

Quality

Security

License

Reuse

Support

deep-rl has a low active ecosystem.

It has 292 star(s) with 188 fork(s). There are 13 watchers for this library.

It had no major release in the last 6 months.

There are 3 open issues and 14 have been closed. On average issues are closed in 19 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of deep-rl is current.

Quality

deep-rl has 0 bugs and 0 code smells.

Security

deep-rl has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

deep-rl code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

deep-rl is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

deep-rl releases are not available. You will need to build from source code and install.

deep-rl has no build file. You will be need to create the build yourself to build the component from source.

deep-rl saves you 109 person hours of effort in developing the same functionality from scratch.

It has 277 lines of code, 26 functions and 2 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed deep-rl and discovered the below as its top functions. This is intended to give you an instant insight into deep-rl implemented functionality, and help decide if they suit your requirements.

Train the model
Sample a batch of data
Build summaries
Adds an experience
Calculate the gradient of the action gradients
Reset the state of the model
Return the number of records in the queue
Get the number of trainable variables

Get all kandi verified functions for this library.

deep-rl Key Features

No Key Features are available at this moment for deep-rl.

deep-rl Examples and Code Snippets

No Code Snippets are available at this moment for deep-rl.

Community Discussions

Trending Discussions on deep-rl

Git says my .gitignore path is invalid and can't be added

QUESTION

Git says my .gitignore path is invalid and can't be added

Asked 2018-Nov-13 at 17:18

I have a very simple problem, and I don't understand what's wrong. I'm trying to stage my .gitignore file for commit, but Git says the path is invalid. Plain as that. My terminal output is pasted below. As you can see, the .gitignore file is indeed there, but I can't add it.

...

ANSWER

Answered 2018-Nov-13 at 17:14

adding a file inside the .git directory. That's a no no.

Source https://stackoverflow.com/questions/53286217

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install deep-rl

You can download it from GitHub.
You can use deep-rl like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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