Metadata-Version: 2.1
Name: scigym
Version: 0.0.2
Summary: SciGym -- The OpenAI Gym for Science: A platform for your scientific reinforcement learning problem.
Home-page: https://github.com/HendrikPN/scigym
Author: HendrikPN
Author-email: hendrik.poulsen-nautrup@uibk.ac.at
License: MIT
Description: **Status:** Development (expect bug fixes, minor updates and new
        environments)
        
        <a href="https://unitary.fund/">
            <img src="https://img.shields.io/badge/Supported%20By-UNITARY%20FUND-brightgreen.svg?style=for-the-badge"
            />
        </a>
        
        # SciGym
        
        <a href="https://scigym.ai">
            <img src="https://raw.githubusercontent.com/HendrikPN/scigym/master/assets/scigym-logo.png" width="120px" align="bottom"
            />
        </a>
        
        **SciGym is a curated library for reinforcement learning environments in science.**
        This is the `scigym` open-source library which gives you access to a standardized set of science environments.
        Visit our webpage at [scigym.ai]. This website serves as a open-source database for science environments: A port where science and reinforcement learning meet.
        
        <a href="https://travis-ci.org/HendrikPN/scigym">
            <img src="https://travis-ci.org/HendrikPN/scigym.svg?branch=master" align="bottom"
            />
        </a>
        
        [See What's New section below](#whats-new)
        
        ## Basics
        
        This project is in line with the policies of the [OpenAI gym]:
        
        There are two basic concepts in reinforcement learning: the environment
        (namely, the outside world) and the agent (namely, the algorithm you are
        writing). The agent sends `actions` to the environment, and
        the environment replies with `observations` and
        `rewards` (that is, a score).
        
        The core `gym` interface is [Env], which is the unified
        environment interface. There is no interface for agents; that part is
        left to you. The following are the `Env` methods you should know:
        
        * `reset(self)`: Reset the environment's state. Returns `observation`.
        * `step(self, action)`: Step the environment by one timestep. Returns `observation`, `reward`, `done`, `info`.
        * `render(self, mode='human', close=False)`: Render one frame of the environment. The default mode will do something human friendly, such as pop up a window. Passing the `close` flag signals the renderer to close any such windows.
        
        ## Installation
        
        There are two main options for the installation of `scigym`:
        
        #### (a) minimal install (recommended)
        
        This method allows you to install the package with no environment specific dependencies, and later add the dependencies for specific environments as you need them.
        
        You can perform a minimal install of `scigym` with:
        
          ```sh
          pip install scigym
          ```
        or
          ```sh
          git clone https://github.com/hendrikpn/scigym.git
          cd scigym
          pip install -e .
          ```
        
        To later add the dependencies for a particular `environment_name`, run the following command:
        
          ```sh
          pip install scigym[environment_name]
          ```
        or from the folder containing `setup.py`
          ```sh
          pip install -e .[environment_name]
          ```
        
        #### (b) full install
        
        This method allows you to install the package, along with all dependencies required for all environments. Be careful, scigym is growing, and this method may install a large number of packages. To view all packages that will be installed during a full install, see the `requirements.txt` file in the root directory. If you wish to perform a full installation you can run:
        
          ```sh
          pip install scigym['all']
          ```
        or
          ```sh
          git clone https://github.com/hendrikpn/scigym.git
          cd scigym
          pip install -e .['all']
          ```
        
        ## Available Environments
        
        At this point we have the following environments available for you to play with:
        
        - [`surfacecode-decoding`](https://github.com/HendrikPN/scigym/tree/master/scigym/envs/quantum_physics/quantum_computing/surfacecode_decoding)
        - [`teleportation`](https://github.com/HendrikPN/scigym/tree/master/scigym/envs/quantum_physics/quantum_computing/teleportation)
        
        ## What's New
        
        - 2019-08-30 This is `scigym` version 0.0.2!
        - 2019-08-30 `scigym` is now available as a package on [PyPI](https://pypi.org/project/scigym/).
        - 2019-08-06 Added [Travis-CI](https://travis-ci.org/HendrikPN/scigym).
        - 2019-08-06: Added [teleportation](https://github.com/HendrikPN/scigym/tree/master/scigym/envs/quantum_physics/quantum_computing/teleportation) environment.
        - 2019-07-21: Added standardized unit testing for all scigym environments.
        - 2019-03-04: Added <a href="https://github.com/R-Sweke/gym-surfacecode">surfacecode</a> environment.
        - 2019-02-09: Initial commit. Hello world :)
        
          [image]: https://img.shields.io/badge/Supported%20By-UNITARY%20FUND-brightgreen.svg?style=for-the-badge
          [OpenAI gym]: https://github.com/openai/gym
          [scigym.ai]: https://scigym.ai
          [Env]: https://github.com/openai/gym/blob/master/gym/core.py
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
Provides-Extra: all
Provides-Extra: surfacecode-decoding
Provides-Extra: teleportation
