RoomEnv-v2 Benchmark (RoomEnv-v2)

The Room environment - v2

[DOI]

We have released a challenging Gymnasium compatible environment. See the paper for more information.

Prerequisites

1. A unix or unix-like x86 machine
2. python 3.10 or higher.
3. Running in a virtual environment (e.g., conda, virtualenv, etc.) is highly recommended so that you don't mess up with the system python.
4. This env is added to the PyPI server. Just run: pip install room-env

Creating a RoomEnv-v2

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 has some good examples.

Running a RoomEnv-v2

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
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Run make test && make style && make quality in the root repo directory, to ensure code quality.
4. Commit your Changes (git commit -m 'Add some AmazingFeature')
5. Push to the Branch (git push origin feature/AmazingFeature)
6. Open a Pull Request

Cite our paper

@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