Reinforcement_Learning_Game

 by   zle1992 Python Version: Current License: No License

kandi X-RAY | Reinforcement_Learning_Game Summary

kandi X-RAY | Reinforcement_Learning_Game Summary

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

Reinforcement_Learning_Game
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            kandi-support Support

              Reinforcement_Learning_Game has a low active ecosystem.
              It has 3 star(s) with 2 fork(s). There are 1 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 1 open issues and 0 have been closed. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of Reinforcement_Learning_Game is current.

            kandi-Quality Quality

              Reinforcement_Learning_Game has no bugs reported.

            kandi-Security Security

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

            kandi-License License

              Reinforcement_Learning_Game does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
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              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              Reinforcement_Learning_Game releases are not available. You will need to build from source code and install.
              Reinforcement_Learning_Game has no build file. You will be need to create the build yourself to build the component from source.
              Installation instructions are not available. Examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed Reinforcement_Learning_Game and discovered the below as its top functions. This is intended to give you an instant insight into Reinforcement_Learning_Game implemented functionality, and help decide if they suit your requirements.
            • Run DeepQ Network
            • Sample from the deque
            • Store a transition
            • Forward one step
            • Train the network
            • Train the model
            • Choose an action
            • Choose an action based on s
            • The main loop
            • Prepare cv2
            • Clean the buffer
            • Get data from the buffer
            • Forward a single frame
            • Check if a player is crash
            • Generate a random pipe
            • Determine if two rects collide with the given rect
            • Load sprite sprites
            • Gets the hit mask of the given image
            • Chooses an action based on the given s
            • Prepare x_t
            Get all kandi verified functions for this library.

            Reinforcement_Learning_Game Key Features

            No Key Features are available at this moment for Reinforcement_Learning_Game.

            Reinforcement_Learning_Game Examples and Code Snippets

            No Code Snippets are available at this moment for Reinforcement_Learning_Game.

            Community Discussions

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

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

            Vulnerabilities

            No vulnerabilities reported

            Install Reinforcement_Learning_Game

            You can download it from GitHub.
            You can use Reinforcement_Learning_Game 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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          • HTTPS

            https://github.com/zle1992/Reinforcement_Learning_Game.git

          • CLI

            gh repo clone zle1992/Reinforcement_Learning_Game

          • sshUrl

            git@github.com:zle1992/Reinforcement_Learning_Game.git

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