gym-miniworld | Simple 3D interior simulator for RL & robotics research | Machine Learning library
kandi X-RAY | gym-miniworld Summary
kandi X-RAY | gym-miniworld Summary
gym-miniworld is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. gym-miniworld has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.
MiniWorld is a minimalistic 3D interior environment simulator for reinforcement learning & robotics research. It can be used to simulate environments with rooms, doors, hallways and various objects (eg: office and home environments, mazes). MiniWorld can be seen as a simpler alternative to VizDoom or DMLab. It is written 100% in Python and designed to be easily modified or extended by students.
MiniWorld is a minimalistic 3D interior environment simulator for reinforcement learning & robotics research. It can be used to simulate environments with rooms, doors, hallways and various objects (eg: office and home environments, mazes). MiniWorld can be seen as a simpler alternative to VizDoom or DMLab. It is written 100% in Python and designed to be easily modified or extended by students.
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Quality
Security
License
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Support
gym-miniworld has a low active ecosystem.
It has 383 star(s) with 72 fork(s). There are 12 watchers for this library.
It had no major release in the last 6 months.
There are 1 open issues and 32 have been closed. On average issues are closed in 45 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of gym-miniworld is current.
Quality
gym-miniworld has 0 bugs and 0 code smells.
Security
gym-miniworld has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
gym-miniworld code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
gym-miniworld is licensed under the Apache-2.0 License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
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gym-miniworld releases are not available. You will need to build from source code and install.
Build file is available. You can build the component from source.
Installation instructions, examples and code snippets are available.
It has 3321 lines of code, 195 functions and 36 files.
It has medium code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed gym-miniworld and discovered the below as its top functions. This is intended to give you an instant insight into gym-miniworld implemented functionality, and help decide if they suit your requirements.
- The main function .
- Generate static data .
- Render the agent .
- get argument parser
- Load the material from a model file .
- Initialize the encoder .
- Evaluate the payoff function .
- Resolve the scene buffer .
- Draws a bounding box .
- Generate the recurrent generator .
Get all kandi verified functions for this library.
gym-miniworld Key Features
No Key Features are available at this moment for gym-miniworld.
gym-miniworld Examples and Code Snippets
No Code Snippets are available at this moment for gym-miniworld.
Community Discussions
Trending Discussions on gym-miniworld
QUESTION
git clone --recursive git@github.com:orybkin/video-gcp.git returning an error
Asked 2022-Jan-07 at 18:49
I am trying to run this code https://github.com/orybkin/video-gcp. It requires me to first clone the GitHub repository
...ANSWER
Answered 2022-Jan-07 at 18:49I cloned the project you specified with submodules using the following command:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install gym-miniworld
You can install all the dependencies with pip3:. If you run into any problems, please take a look at the troubleshooting guide, and if you're still stuck, please open an issue on this repository to let us know something is wrong.
Python 3.5+
OpenAI Gym
NumPy
Pyglet (OpenGL 3D graphics)
GPU for 3D graphics acceleration (optional)
Python 3.5+
OpenAI Gym
NumPy
Pyglet (OpenGL 3D graphics)
GPU for 3D graphics acceleration (optional)
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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