roboschool | source software for robot simulation | Robotics library

 by   openai Python Version: 1.0.49 License: Non-SPDX

kandi X-RAY | roboschool Summary

kandi X-RAY | roboschool Summary

roboschool is a Python library typically used in Automation, Robotics applications. roboschool has no bugs, it has no vulnerabilities, it has build file available and it has medium support. However roboschool has a Non-SPDX License. You can install using 'pip install roboschool' or download it from GitHub, PyPI.

Release blog post is here:.
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    Quality
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            kandi-support Support

              roboschool has a medium active ecosystem.
              It has 2062 star(s) with 486 fork(s). There are 261 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 74 open issues and 109 have been closed. On average issues are closed in 27 days. There are 8 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of roboschool is 1.0.49

            kandi-Quality Quality

              roboschool has 0 bugs and 0 code smells.

            kandi-Security Security

              roboschool has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              roboschool code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              roboschool has a Non-SPDX License.
              Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

            kandi-Reuse Reuse

              roboschool releases are not available. You will need to build from source code and install.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.
              roboschool saves you 3112 person hours of effort in developing the same functionality from scratch.
              It has 6701 lines of code, 242 functions and 54 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed roboschool and discovered the below as its top functions. This is intended to give you an instant insight into roboschool implemented functionality, and help decide if they suit your requirements.
            • Test function for triangulation
            • Push a vector onto the stack
            • Push a single value onto the stack
            • Push VT to TV
            • Perform a step
            • Calculate the state of the robot
            • Apply an action to the motors
            • Perform a step
            • Runs the game
            • Example testing example
            • Actual activation function
            • Performs a step
            • Performs a step
            • Test the game
            • Reset robot specific attributes
            • Test the convex hull
            • Reset robot specific attributes
            • Reset the game
            • Test the contour
            • Set plate parameters
            • Reset the robot
            • Calculate global step
            • Resets the robot
            • Generate an alive cage
            • Restart an episode
            • Tomographic parameters
            • Serve forever
            Get all kandi verified functions for this library.

            roboschool Key Features

            No Key Features are available at this moment for roboschool.

            roboschool Examples and Code Snippets

            Training MULTIPOLAR in other environments
            Jupyter Notebookdot img1Lines of Code : 60dot img1License : Non-SPDX (NOASSERTION)
            copy iconCopy
            python random_env_dynamic_train_cmd_gen.py --num-samples 100 --algo ppo2 --seed 0 --env RoboschoolHopper-v1 \
            --params-ranges leg_length,0.35,0.65 foot_length,0.29,0.49 thigh_length,0.35,0.55 torso_length,0.3,0.5 size,0.7,1.1 damping,0.5,4 friction,0  
            copy iconCopy
            git clone --recursive https://github.com/MichaelKonobeev/neuralode-rl.git
            cd neuralode-rl
            pip install -r requirements.txt
            
            pip install roboschool==1.0.48
            
            python run-mujoco.py --env-id HalfCheetah-v3 --logdir logdir/mlp/half-cheetah.00
            # OR
            python ru  
            3. Using the ,1. Introduction
            Pythondot img3Lines of Code : 16dot img3no licencesLicense : No License
            copy iconCopy
            from RL import Trainer
            
            model = Trainer('ENV_NAME').create_model() # ENV_NAME -> the name of the class of your environment
            model.train()                              # Train the model as specified in the configuration file and save it in a unique   

            Community Discussions

            QUESTION

            ImportError: cannot import name 'cpp_household' from 'roboschool'
            Asked 2020-Jun-13 at 09:03

            pls help me. I can not import lib

            roboschool 1.0.49 gym==0.15.4

            input

            ...

            ANSWER

            Answered 2020-Jun-13 at 09:03

            It looks like you're on Windows. As mentioned here, roboschool doesn't work on Windows, unfortunately.

            Source https://stackoverflow.com/questions/62354041

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

            Vulnerabilities

            No vulnerabilities reported

            Install roboschool

            If you are running Ubuntu or Debian Linux, or OS X, the easiest path to install roboschool is via pip (:. Note: in a headless machine (e.g. docker container) you may need to install graphics libraries; this can be achieved via apt-get install libgl1-mesa-dev. If you are running some other Linux/Unix distro, or want the latest and the greatest code, or want to tweak the compiler optimization options, read on…​.
            The compiler options are configured in the [Makefile](roboschool/cpp-household/Makefile). Feel free to tinker with them or leave those as is. To compile the project code, and then install it as a python package, use the following:.

            Support

            A lot of the issues during installation from source are due to missing / incorrect PKG_CONFIG_PATH variable. If the command pkg-config --cflags Qt5OpenGL assimp bullet shows an error, you can try manually finding missing *.pc files (for instance, for if the pkg-config complains about assimp, run find / -name "assimp.pc" - this is a bit bruteforce, but it works :)) and then adding folder with that files to PKG_CONFIG_PATH. Sometime distros of linux may complain about generated code being not platform-independent, and ask you to recompile something with -fPIC option (this was seen on older versions of CentOS). In that case, try removing -march=native compilation option in the Makefile. On the systems with nvidia drivers present, roboschool sometimes is not be able to find hardware-accelerated libraries. If you see errors like.
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            Install
          • PyPI

            pip install roboschool

          • CLONE
          • HTTPS

            https://github.com/openai/roboschool.git

          • CLI

            gh repo clone openai/roboschool

          • sshUrl

            git@github.com:openai/roboschool.git

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