A tool to help train reinforcement learning models to handle novel environments.
Polycraft AI Lab consists of a wrapper for Polycraft World game environments. These environments can used to train RL models that respond to novel tasks and scenarios.
First, download Polycraft AI Lab using pip:
pip install polycraft-lab
This downloads the polycraft-lab
package from pip, which contains tools to
easily set up and manage the Polycraft game client.
To install the most recent changes (experimental version):
pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://test.pypi.org/simple/ polycraft-lab
Alternatively, to install the latest version from source:
git clone https://github.com/PolycraftWorld/polycraft-ai-lab
pip install ./polycraft-ai-lab
If you installed PAL using method (1a), this isn't required.
Run the following command and follow the guided instructions:
pal init
This will automatically download Polycraft World to your local machine and initialize up other configuration files.
Now train your agent like you would do with any other gym-style environment:
from polycraft_lab import make
env = make('pogo_stick')
observation = env.reset()
for _ in range(1000):
env.render()
action = env.action_space.sample() # your agent here (this takes random actions)
observation, reward, done, info = env.step(action)
if done:
observation = env.reset()
env.close()
Polycraft AI Lab also contains a wrapper [WIP] to start experiment creation from the command line. The following begins the experiment creation process by launching Minecraft:
python -m polycraft_lab.ect --create EXPERIMENT_NAME --launch
Running python -m polycraft_lab.ect --create EXPERIMENT_NAME
will allow you to
create simpler experiments, allowing configuration of more high-level domain
attributes, like action space and a preconfigured goal, such as finding
diamonds as quickly as possible.
Clone out the repository:
git clone https://github.com/PolycraftWorld/polycraft-ai-lab.git
Optionally, you can create a virtual environment to store dependencies.
In any case, install the dependencies:
pip install -r requirements.txt
Alternatively, a virtual environment can be created with the necessary dependencies by running:
cd polycraft-ai-lab
pipenv install
Polycraft AI Lab will be distributed using pip.
The easy way to upload to upload to the test PyPI index:
./release.sh
To release to the live PyPI index:
./release.sh --release