| Name | tile-match-gym JSON |
| Version |
0.0.5
JSON |
| download |
| home_page | None |
| Summary | A set of reinforcement learning environments for tile matching games, consistent with the OpenAI Gym API. |
| upload_time | 2024-04-05 13:37:51 |
| maintainer | None |
| docs_url | None |
| author | James Elson |
| requires_python | >=3.8 |
| license | Copyright (c) 2023, Akshil Patel Copyright (c) 2023, James Elson All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of tile-match-gym nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
| keywords |
reinforcement-learning
gym
tile-matching
games
rl
openai-gym
gymnasium
rl-environment
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Tile Matching Reinforcement Learning Environments
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:
- Large action spaces
- Intuitive action hierarchies
- Procedurally generated levels
- Structured complex stochasticity in transition dynamics
## Installation
You can install the package via pip:
```pip install tile-match-gym```
## Example Usage
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
```
## Citation
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}
}
```
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"description": "# Tile Matching Reinforcement Learning Environments\n\nWelcome 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.\n\nThis genre of games is characterised by the following features, which we find useful for reinforcement learning research:\n\n- Large action spaces\n- Intuitive action hierarchies\n- Procedurally generated levels\n- Structured complex stochasticity in transition dynamics\n\n## Installation\n\nYou can install the package via pip:\n\n```pip install tile-match-gym```\n\n## Example Usage\n\nWe follow the the Farama Foundation Gymnasium API:\n\n```\nfrom tile_match_gym.tile_match_env import TileMatchEnv\n\nenv = TileMatchEnv(\n num_rows=10, \n num_cols=10, \n num_colours=4, \n num_moves=30, \n colourless_specials=[], \n colour_specials=[], \n seed=2\n )\n\nobs, _ = env.reset()\n\nwhile True:\n action = env.action_space.sample()\n next_obs, reward, done, truncated, info = env.step(action)\n if done:\n break\n else:\n next_obs = obs\n```\n\n## Citation\n\nWe'd love it if you use our package for your research! If you do use code from this repository please cite us as below:\n\n```\n@software{tile_match_gym,\n author = {Patel, Akshil and Elson, James},\n title = {{Tile Matching Game Reinforcement Learning Environments}},\n url = {https://github.com/akshilpatel/tile-match-gym},\n version = {0.0.5},\n year = {2023}\n }\n```\n",
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