RoomEnv-v2
홈페이지 · 논문 1편
# The Room environment - v2
We have released a challenging Gymnasium compatible
environment. See the paper for more information.
<img src="figures/room-layout-xl.png" alt="" style="width: 50%;"/><img src="figures/room-layout-kg-xl.png" alt="" style="width: 50%;"/>
## Prerequisites
1. A unix or unix-like x86 machine
1. python 3.10 or higher.
1. Running in a virtual environment (e.g., conda, virtualenv, etc.) is highly recommended so that you don't mess up with the system python.
1. This env is added to the PyPI server. Just run: pip install room-env
## Creating a RoomEnv-v2
``python
import random
from room_env.create_room_v2 import RoomCreator
room_creator = RoomCreator(
filename="dev",
grid_length=7,
num_rooms=32,
num_static_objects=8,
num_independent_objects=8,
num_dependent_objects=8,
room_prob=0.5,
minimum_transition_stay_prob=0.6,
static_object_in_every_room=False,
give_fake_names=False,
)
room_creator.run()
`
[./room-env-v2.ipynb](./room-env-v2.ipynb) has some good examples.
## Running a RoomEnv-v2
`python
import gymnasium as gym
import random
env = gym.make("room_env:RoomEnv-v2", room_size="l")
observations, info = env.reset()
rewards = 0
while True:
observations, reward, done, truncated, info = env.step(
(
["random answer"] * len(observations["questions"]),
random.choice(["north", "east", "south", "west", "stay"]),
)
)
rewards += reward
if done or truncated:
break
# You can also get the map of the rooms
room_layout = env.unwrapped.return_room_layout(exclude_walls=True)
`
Take a look at this repo for an actual
interaction with this environment to learn a policy.
## Contributing
Contributions are what make the open source community such an amazing place to be learn,
inspire, and create. Any contributions you make are greatly appreciated.
1. Fork the Project
1. Create your Feature Branch (git checkout -b feature/AmazingFeature)
1. Run make test && make style && make quality in the root repo directory,
to ensure code quality.
1. Commit your Changes (git commit -m 'Add some AmazingFeature')
1. Push to the Branch (git push origin feature/AmazingFeature)
1. Open a Pull Request
## Cite our paper
`bibtex
@misc{kim2024leveragingknowledgegraphbasedhumanlike,
title={Leveraging Knowledge Graph-Based Human-Like Memory Systems to Solve Partially Observable Markov Decision Processes},
author={Taewoon Kim and Vincent François-Lavet and Michael Cochez},
year={2024},
eprint={2408.05861},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2408.05861},
}
``
## Authors
- Taewoon Kim
- Michael Cochez
- Vincent Francois-Lavet
## License
MIT
