DeepRacerRL | reinforcement learning models reward functions

by ugurkanates Python Version: Current License: MIT

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kandi X-RAY | DeepRacerRL Summary

DeepRacerRL is a Python library. DeepRacerRL has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However DeepRacerRL build file is not available. You can download it from GitHub.

My reinforcement learning models + reward functions for beginners to understand Amazon DeepRacer a bit more. My first model tested on actual race track at Istanbul IX.0 Accenture offices and it performed stable enough. My 11 second virtual track record is not perfectly stable in actual environments due light and color saturation of physical track. (not every track is good as reInvent event tracks bear in mind). I included both reward functions + model files for you to test out. Any question can hit me with e-mail. A small clip from actual track at event Both model files have their enviroment details ,speed selection , angle selection etc listed in their respective files.

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DeepRacerRL has a low active ecosystem.

It has 6 star(s) with 1 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

DeepRacerRL has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of DeepRacerRL is current.

Quality

DeepRacerRL has 0 bugs and 0 code smells.

Security

DeepRacerRL has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

DeepRacerRL code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

DeepRacerRL is licensed under the MIT License. This license is Permissive.

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

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DeepRacerRL releases are not available. You will need to build from source code and install.

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

It has 40 lines of code, 2 functions and 2 files.

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

Top functions reviewed by kandi - BETA

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

Return reward function .

Get all kandi verified functions for this library.

DeepRacerRL Key Features

No Key Features are available at this moment for DeepRacerRL.

DeepRacerRL Examples and Code Snippets

No Code Snippets are available at this moment for DeepRacerRL.

Community Discussions

No Community Discussions are available at this moment for DeepRacerRL.Refer to stack overflow page for discussions.

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

Vulnerabilities

No vulnerabilities reported

Install DeepRacerRL

You can download it from GitHub.
You can use DeepRacerRL 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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