Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos
Install requirements with:
pip install git+https://github.com/minerllabs/[email protected] pip install -r requirements.txt
To run the code, call
python run_agent.py --model [path to .model file] --weights [path to .weight file]
After loading up, you should see a window of the agent playing Minecraft.
Below are the model files and weights files for various pre-trained Minecraft models. The 1x, 2x and 3x model files correspond to their respective model weights width.
These models are trained on video demonstrations of humans playing Minecraft using behavioral cloning (BC) and are more general than later models which use reinforcement learning (RL) to further optimize the policy. Foundational models are trained across all videos in a single training run while house and early game models refine their respective size foundational model further using either the housebuilding contractor data or early game video sub-set. See the paper linked above for more details.
These models further refine the above demonstration based models with a reward function targeted at obtaining diamond pickaxes. While less general then the behavioral cloning models, these models have the benefit of interacting with the environment using a reward function and excel at progressing through the tech tree quickly. See the paper for more information on how they were trained and the exact reward schedule.
We are currently working on to release contractor data collected over the course of the project. Links to index files with more information will be linked here as the data is released.
This was a large effort by a dedicated team at OpenAI: Bowen Baker, Ilge Akkaya, Peter Zhokhov, Joost Huizinga, Jie Tang, Adrien Ecoffet, Brandon Houghton, Raul Sampedro, Jeff Clune The code here represents a minimal version of our model code which was prepared by Anssi Kanervisto and others so that these models could be used as part of the MineRL BASALT competition.