ScienceWorld is a text-based virtual environment centered around accomplishing tasks from the standardized elementary science curriculum. This code accompanies the paper ScienceWorld: Is your Textual Agent Smarter than a 5th grader?.

You can try ScienceWorld yourself via our HuggingFace Space or read some of the playthrough transcripts.

@misc{scienceworld2022,
    title={ScienceWorld: Is your Agent Smarter than a 5th Grader?},
    author={Ruoyao Wang and Peter Jansen and Marc-Alexandre C{\^o}t{\'e} and Prithviraj Ammanabrolu},
    year={2022},
    eprint={2203.07540},
    archivePrefix={arXiv},
    primaryClass={cs.CL},
    url={https://arxiv.org/abs/2203.07540}
}

Before running: You will have to have Java 1.8 installed on your system (shipped with most linux distributions) and Python 3.8 . We recommend creating a conda environment like this:

conda create --name scienceworld python=3.8
conda activate scienceworld

Then, install ScienceWorld either from PyPi:

pip install scienceworld

or from source in development mode:

git clone https://github.com/allenai/ScienceWorld.git
cd ScienceWorld
pip install .

Run an example random agent, on task 13 (classification: place a non-living thing in a box), for 5 episodes:

python examples/random_agent.py --task-num=13 --num-episodes=5 --simplifications-preset easy

Run a user console where you can interact with the environment, on task 3 (change of state: melting):

python examples/human.py --task-num=3 --num-episodes=5

A web server demo is also available, that allows running a ScienceWorld user console that can be interacted with in a web browser.

To run the web server demo:

conda create --name scienceworld python=3.8
conda activate scienceworld
pip install scienceworld[webserver]

Run the web server:

python examples/scienceworld-web-server-example.py

Point your web browser to: localhost:8080

ScienceWorld is written in Scala (2.12.9), and compiles using sbt into a JAR file that is run with Java. For convenience, a Python API is provided (Python >= 3.8), which interfaces using the py4j package.

If you modified the Scala code, you can recompile the JAR file by running:

./simulator/package.sh
pip install -e .

The tasks are listed in the table below along with their number of variations. Either the task ID or its name can be used to a task with env.load().

DRRN: https://github.com/cognitiveailab/drrn-scienceworld

KG-A2C: https://github.com/cognitiveailab/kga2c-scienceworld

CALM: https://github.com/cognitiveailab/calm-scienceworld

Behavior Cloning and Decision Transformer: https://github.com/cognitiveailab/t5-scienceworld