pyfiction 0.1.2

Introduction

Text-based games, more commonly known as interactive fiction (IF), come in various formats and use different interpreters that aren’t often compatible with each other.

The goal of pyfiction is to wrap the functionality of different IF simulators and to provide a universal API for text games for research purposes.

Pyfiction also includes sample agents that can learn to play the supplied text games.

Requirements

• Python 3.6 (older version might work but they aren’t tested, Python 3.5 tested briefly)

• text-games submodule for games ‘Saving John’ and ‘Machine of Death’

• keras, tensorflow and their dependencies for launching the example agents

• selenium and chromedriver for running the HTML-based games

• h5py for saving and loading models

• pydot for visualising model graphs

Installation

To install the latest published version, simply run:

pip install pyfiction

To install the bleeding-edge version from this repository, run:

git clone --recursive https://github.com/MikulasZelinka/pyfiction
cd pyfiction
pip install -e .

Interface

pyfiction provides a simple agent-environment interface for text-game environments.

Examples

To run any example (from the game list below or from this list), simply run:

python file.py

on these files:

• Interactively play any of the supported games,

• An SSAQN (siamese state-action q-network) agent [3] learning to play multiple games at once,

• Interactive testing of state-action Q-values of the above agent trained on the supported games,

or on one of the supported games below.

Currently supported games

The following links lead to training experiments of the SSAQN agent on the supported games:

• Saving John

• Machine of Death

• Cat Simulator 2016 [HTML-based]

• Star Court [HTML-based]

• The Red Hair [HTML-based]

• Transit [HTML-based]

Additionally, the plan is to integrate the individual games to the OpenAI Gym, see the pull request and the text_games branch for details.

References

[1] Language Understanding for Text-based Games Using Deep Reinforcement Learning

[2] Deep Reinforcement Learning with a Natural Language Action Space

[3] Using reinforcement learning to learn how to play text-based games (Master thesis, available in 09/2017)

[4] Reinforcement Learning: An Introduction

Status

Version 0.1.2, Alpha