control-of-jump-systems-based-on-reinforcement-learning | Code for the paper “ Control Strategy of Speed Servo
kandi X-RAY | control-of-jump-systems-based-on-reinforcement-learning Summary
kandi X-RAY | control-of-jump-systems-based-on-reinforcement-learning Summary
control-of-jump-systems-based-on-reinforcement-learning is a Python library. control-of-jump-systems-based-on-reinforcement-learning has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.
This is the implementation of the paper “Control Strategy of Speed Servo Systems Based on Deep Reinforcement Learning”. For more paper information, please checkout the the paper Link.
This is the implementation of the paper “Control Strategy of Speed Servo Systems Based on Deep Reinforcement Learning”. For more paper information, please checkout the the paper Link.
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control-of-jump-systems-based-on-reinforcement-learning has a low active ecosystem.
It has 17 star(s) with 3 fork(s). There are 2 watchers for this library.
It had no major release in the last 6 months.
control-of-jump-systems-based-on-reinforcement-learning has no issues reported. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of control-of-jump-systems-based-on-reinforcement-learning is current.
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control-of-jump-systems-based-on-reinforcement-learning has no bugs reported.
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control-of-jump-systems-based-on-reinforcement-learning has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
License
control-of-jump-systems-based-on-reinforcement-learning does not have a standard license declared.
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Without a license, all rights are reserved, and you cannot use the library in your applications.
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control-of-jump-systems-based-on-reinforcement-learning 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 are not available. Examples and code snippets are available.
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control-of-jump-systems-based-on-reinforcement-learning Key Features
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control-of-jump-systems-based-on-reinforcement-learning Examples and Code Snippets
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Install control-of-jump-systems-based-on-reinforcement-learning
You can download it from GitHub.
You can use control-of-jump-systems-based-on-reinforcement-learning 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.
You can use control-of-jump-systems-based-on-reinforcement-learning 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.
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