drl_navigation | Deep Reinforcement Learning agent with tarjet network

by arutaku HTML Version: Current License: GPL-3.0

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

kandi X-RAY | drl_navigation Summary

drl_navigation is a HTML library. drl_navigation has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. You can download it from GitHub.

For this project, I have trained an agent to navigate and collect bananas. A reward of +1 is provided for collecting a yellow banana, and a reward of -1 is provided for collecting a blue banana. Thus, the goal of your agent is to collect as many yellow bananas as possible while avoiding blue bananas.

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Quality

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Support

drl_navigation has a low active ecosystem.

It has 0 star(s) with 0 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

drl_navigation has no issues reported. There are 12 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of drl_navigation is current.

Quality

drl_navigation has no bugs reported.

Security

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

License

drl_navigation is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

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

Installation instructions, examples and code snippets are available.

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drl_navigation Key Features

No Key Features are available at this moment for drl_navigation.

drl_navigation Examples and Code Snippets

No Code Snippets are available at this moment for drl_navigation.

Community Discussions

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Vulnerabilities

No vulnerabilities reported

Install drl_navigation

I have used Linux. You can download the version for your SO, but remember to point to your Banana environment folder:. Due to issues with conda, not only environment.yml is provided. Another file (requirements.txt) is also attached and should be taken into account. Next with pip: pip install -r requirements.txt.
Download the environment from one of the links below. You need only select the environment that matches your operating system: Linux: click here Mac OSX: click here Windows (32-bit): click here Windows (64-bit): click here (For Windows users) Check out this link if you need help with determining if your computer is running a 32-bit version or 64-bit version of the Windows operating system. (For AWS) If you'd like to train the agent on AWS (and have not enabled a virtual screen), then please use this link to obtain the environment.
Place the file in the unziped folder, and unzip (or decompress) the file.
Create a virtual environment with anaconda and install packages: conda env create -f environment.yml.
Activate the virtual environment: source activate <name of the env>.
Install more packages:
Launch jupyter notebook: jupyter notebook Navigation.ipynb
Execute cells: just first cell (for imports):

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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