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A set of reinforcement learning environments for tile matching games, consistent with the OpenAI Gym API.
Readme
Welcome to the Reinforcement Learning Environments for Tile Matching Games repository! Here you can find a collection of tile matching game environments (like Bejeweled or Candy Crush), poised to push reinforcement learning research forwards.
This genre of games is characterised by the following features, which we find useful for reinforcement learning research:
You can install the package via pip:
pip install tile-match-gym
We follow the the Farama Foundation Gymnasium API:
from tile_match_gym.tile_match_env import TileMatchEnv
env = TileMatchEnv(
num_rows=10,
num_cols=10,
num_colours=4,
num_moves=30,
colourless_specials=[],
colour_specials=[],
seed=2
)
obs, _ = env.reset()
while True:
action = env.action_space.sample()
next_obs, reward, done, truncated, info = env.step(action)
if done:
break
else:
next_obs = obs
We'd love it if you use our package for your research! If you do use code from this repository please cite us as below:
@software{tile_match_gym,
author = {Patel, Akshil and Elson, James},
title = {{Tile Matching Game Reinforcement Learning Environments}},
url = {https://github.com/akshilpatel/tile-match-gym},
version = {0.0.5},
year = {2023}
}
FAQs
A set of reinforcement learning environments for tile matching games, consistent with the OpenAI Gym API.
We found that tile-match-gym demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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