semvec/pypi-packages huggingface dataset:
eurocodedesign 0.0.1
Typed python framework for eurocode calculations.
Requires Python: >=3.10
Keywords: eurocodedesign

==============
eurocodedesign
==============

Typed python framework for eurocode calculations.



Features
--------

* TODO

Credits
-------

This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.

.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage


=======
History
=======

0.0.1 (2023-03-14)
------------------

* First release on PyPI.
token-information 1.0.0
token_information is a Python Package created for scraping discord account data with the use of an account token.
Requires Python: >=3.9
Keywords: spy404,discord,token_information

<h1 align="center">
  Discord token information library
</h1>

Steps to install:
- <pre> pip3 install token_information </pre>

Commands:
- <pre> token_information.user(token) </pre>
- <pre> token_information.payment(token) </pre>
- <pre> token_information.username(token) </pre>
- <pre> token_information.userid(token) </pre>
- <pre> token_information.phonenumber(token) </pre>
- <pre> token_information.avatar(token) </pre>
- <pre> token_information.email(token) </pre>
- <pre> token_information.auth(token) </pre>
- <pre> token_information.flags(token) </pre>
- <pre> token_information.language(token) </pre>
- <pre> token_information.verified(token) </pre>
- <pre> token_information.login(token) </pre>
- <pre> token_information.creation_date(token) </pre>
- <pre> token_information.nitro(token) </pre>

<div align="center">
  <a href="https://github.com/spy404/">
    <img src="http://ForTheBadge.com/images/badges/made-with-python.svg" />
  </a>
</div>
rlgridworld 0.1004
This is a simple yet efficient, highly customizable grid-world implementation to run reinforcement learning algorithms.

RLGridWorld
===========

This is a simple yet efficient, highly customizable grid-world implementation to run reinforcement learning algorithms.

The official documentation is here `https://rlgridworld.readthedocs.io/ <https://rlgridworld.readthedocs.io/>`_

install with

.. code-block:: bash
    
    pip install rlgridworld


Environment
-----------

You can simply use a string like 

.. code-block:: text

    W H T O W
    W O O H W
    W O A O W
    W O O T W
    W W W W W

to represent a grid-world, where

    * A: Agent
    * T: Target location
    * O: Empty Ground spot (where the agent can step on and stay)
    * W: Wall
    * H: Hole (where the agent will fall if it steps in)

The single_rgb_array rendering of which is:

.. image:: imgs/ExampleFile.png
  :width: 400
  :alt: Alternative text

The goal of the agent is to reach one of the Target locations without falling into a hole or falling out of the edge.
(More pre-configured environments can be found in EnvSettings)


Actions
-------

The actions can be continuous or discrete. The agent can also move diagonally.
The details can be found in the Action class in rlgridworld/gridenv.py

    * Continuous: Action is a tuple of length 2, where the first element is the x-axis and the second element is the y-axis
    * Discrete: Action can be chosen from ['UP', 'DOWN', 'RIGHT', 'LEFT', 'UPRIGHT', 'UPLEFT', 'DOWNRIGHT', 'DOWNLEFT']


Reward
------

Customizable with r_fall_off, r_reach_target, r_timeout, r_continue.
The details can be found in the __init__ function of class GridEnv in rlgridworld/gridenv.py