{"url":"/sota/roomenv-v2-on-roomenv-v2","task":{"name":"RoomEnv-v2","url":"/task/roomenv-v2","note":null},"dataset":{"name":"RoomEnv-v2","url":"/dataset/roomenv-v2"},"category":"Computer Vision","categories":["Computer Code","Computer Vision","Knowledge Base","Methodology"],"category_note":null,"description":"# The Room environment - v2\r\n\r\n[![DOI](https://img.shields.io/badge/Paper-PDF-red.svg)](https://arxiv.org/pdf/2408.05861)\r\n\r\nWe have released a challenging [Gymnasium](https://www.gymlibrary.dev/) compatible\r\nenvironment. See the paper for more information.\r\n\r\n<img src=\"figures/room-layout-xl.png\" alt=\"\" style=\"width: 50%;\"/><img src=\"figures/room-layout-kg-xl.png\" alt=\"\" style=\"width: 50%;\"/>\r\n\r\n## Prerequisites\r\n\r\n1. A unix or unix-like x86 machine\r\n1. python 3.10 or higher.\r\n1. Running in a virtual environment (e.g., conda, virtualenv, etc.) is highly recommended so that you don't mess up with the system python.\r\n1. This env is added to the PyPI server. Just run: `pip install room-env`\r\n\r\n## Creating a RoomEnv-v2\r\n\r\n```python\r\nimport random\r\nfrom room_env.create_room_v2 import RoomCreator\r\n\r\nroom_creator = RoomCreator(\r\n    filename=\"dev\",\r\n    grid_length=7,\r\n    num_rooms=32,\r\n    num_static_objects=8,\r\n    num_independent_objects=8,\r\n    num_dependent_objects=8,\r\n    room_prob=0.5,\r\n    minimum_transition_stay_prob=0.6,\r\n    static_object_in_every_room=False,\r\n    give_fake_names=False,\r\n)\r\nroom_creator.run()\r\n```\r\n\r\n[`./room-env-v2.ipynb`](./room-env-v2.ipynb) has some good examples.\r\n\r\n## Running a RoomEnv-v2\r\n\r\n```python\r\nimport gymnasium as gym\r\nimport random\r\n\r\nenv = gym.make(\"room_env:RoomEnv-v2\", room_size=\"l\")\r\nobservations, info = env.reset()\r\nrewards = 0\r\n\r\nwhile True:\r\n    observations, reward, done, truncated, info = env.step(\r\n        (\r\n            [\"random answer\"] * len(observations[\"questions\"]),\r\n            random.choice([\"north\", \"east\", \"south\", \"west\", \"stay\"]),\r\n        )\r\n    )\r\n    rewards += reward\r\n    if done or truncated:\r\n        break\r\n\r\n# You can also get the map of the rooms\r\nroom_layout = env.unwrapped.return_room_layout(exclude_walls=True)\r\n```\r\n\r\nTake a look at [this repo](https://github.com/humemai/humemai) for an actual\r\ninteraction with this environment to learn a policy.\r\n\r\n## Contributing\r\n\r\nContributions are what make the open source community such an amazing place to be learn,\r\ninspire, and create. Any contributions you make are **greatly appreciated**.\r\n\r\n1. Fork the Project\r\n1. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)\r\n1. Run `make test && make style && make quality` in the root repo directory,\r\n   to ensure code quality.\r\n1. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)\r\n1. Push to the Branch (`git push origin feature/AmazingFeature`)\r\n1. Open a Pull Request\r\n\r\n## [Cite our paper](https://arxiv.org/abs/2408.05861)\r\n\r\n```bibtex\r\n@misc{kim2024leveragingknowledgegraphbasedhumanlike,\r\n      title={Leveraging Knowledge Graph-Based Human-Like Memory Systems to Solve Partially Observable Markov Decision Processes},\r\n      author={Taewoon Kim and Vincent François-Lavet and Michael Cochez},\r\n      year={2024},\r\n      eprint={2408.05861},\r\n      archivePrefix={arXiv},\r\n      primaryClass={cs.AI},\r\n      url={https://arxiv.org/abs/2408.05861},\r\n}\r\n```\r\n\r\n## Authors\r\n\r\n- [Taewoon Kim](https://taewoon.kim/)\r\n- [Michael Cochez](https://www.cochez.nl/)\r\n- [Vincent Francois-Lavet](http://vincent.francois-l.be/)\r\n\r\n## License\r\n\r\n[MIT](https://choosealicense.com/licenses/mit/)","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["final agent reward"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"final agent reward":null}},"counts":{"rows":2,"rows_with_code":2,"rows_with_paper_page":2,"rows_dated":2,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"HumemAI-capacity=48","metrics":{"final agent reward":"235"},"uses_additional_data":false,"paper_date":"2024-08-11","paper":"/paper/leveraging-knowledge-graph-based-human-like","paper_url":"https://arxiv.org/abs/2408.05861v2","paper_title":"Leveraging Knowledge Graph-Based Human-Like Memory Systems to Solve Partially Observable Markov Decision Processes","code":"https://github.com/humemai/agent-room-env-v2-lstm","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"Baseline-capacity=48","metrics":{"final agent reward":"200"},"uses_additional_data":false,"paper_date":"2024-08-11","paper":"/paper/leveraging-knowledge-graph-based-human-like","paper_url":"https://arxiv.org/abs/2408.05861v2","paper_title":"Leveraging Knowledge Graph-Based Human-Like Memory Systems to Solve Partially Observable Markov Decision Processes","code":"https://github.com/humemai/agent-room-env-v2-lstm","n_code_links":1,"syntology":null}],"since_archive":{"claim":"Results that newer papers report for their own method, placed here by Syntology. 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