semvec/pypi-packages huggingface dataset:
gym-games 1.0.3
This is a gym version of various games for reinforcenment learning.
License: MIT
Requires Python: >=3.5
Keywords: AI,Reinforcement Learning,Games,Pygame,MinAtar

# Gym Games

This is a gym compatible version of various games for reinforcenment learning.

For [PyGame Learning Environment](https://pygame-learning-environment.readthedocs.io/en/latest/user/games.html), the default observation is a non-visual state representation of the game. 

For [MinAtar](https://github.com/kenjyoung/MinAtar), the default observation is a visual input of the game.

## Environments

- PyGame learning environment:
  - Catcher-PLE-v0
  - FlappyBird-PLE-v0
  - Pixelcopter-PLE-v0
  - PuckWorld-PLE-v0
  - Pong-PLE-v0

- MinAtar:
  - Asterix-MinAtar-v0
  - Breakout-MinAtar-v0
  - Freeway-MinAtar-v0
  - Seaquest-MinAtar-v0
  - Space_invaders-MinAtar-v0

## Installation

### Gym

Please read the instruction [here](https://github.com/openai/gym).

### Pygame

- On OSX:

      brew install sdl sdl_ttf sdl_image sdl_mixer portmidi
      pip install pygame

- On Ubuntu:

      sudo apt-get -y install python-pygame
      pip install pygame

- Others: Please read the instruction [here](http://www.pygame.org/wiki/GettingStarted#Pygame%20Installation).

### PyGame Learning Environment

    pip install git+https://github.com/ntasfi/PyGame-Learning-Environment.git

## MinAtar

    pip install git+https://github.com/kenjyoung/MinAtar.git

### Gym-games

  - Install from source:

        pip install git+https://github.com/qlan3/gym-games.git

  - Install from PyPi:

        pip install gym-games

## Example

Run ``python test.py``.


## Cite

Please use this bibtex to cite this repo:

    @misc{gym-games,
    author = {Qingfeng, Lan},
    title = {Gym Compatible Games for Reinforcenment Learning},
    year = {2019},
    publisher = {GitHub},
    journal = {GitHub Repository},
    howpublished = {\url{https://github.com/qlan3/gym-games}}
    }

## References

- [gym](https://github.com/openai/gym/tree/master/)
- [gym-ple](https://github.com/lusob/gym-ple)
- [SRNN](https://github.com/VincentLiu3/SRNN)
- [MinAtar](https://github.com/kenjyoung/MinAtar)
plnlp 1.0.1
Keyword Recognition and Pre-processing
License: MIT

In name entity recognition projects, we need to pre-process the character in sentences, which need to be converted into numeric features. In this package, we represent each character in two features: (1) vocabulary id, and (2) capitalization type. For example, the first record in the raw data is 


  (['EU', 'rejects', 'German', 'call', 'to', 'boycott', 'British', 'lamb', '.'],
    ['ORG', 'O', 'MISC', 'O', 'O', 'O', 'MISC', 'O', 'O'])


the first list in this tuple is the original sentence, the second list in the tuple is the name entity for each character.

Using our package, the raw data bacome

  ([[1, 12],
     [2, 11],
     [3, 13],
     [4, 11],
     [5, 11],
     [6, 11],
     [7, 13],
     [8, 11],
     [9, 14]],
    [1, 4, 3, 4, 4, 4, 3, 4, 4])


in which the first list is the numeric features for the characters (id, capitalization type), the second list is the corresponding numeric labels for the name entity type. 

At the same time, you can also retrieve the vocabulary dictionary built from your sentence data.

To do this, run the following code in terminal

    python -m plnlp './plnlp/tiny.conll' './plnlp/tiny.conll'

The first and second arguments are the path of the train dataset and the development dataset, respectively.

The resulting datasets will be written and saved under your current working directory in a csv file.