Documentation is hosted by github.io.
After cloning this repo, run:
pip install -e .[dev]
To test if the installation was successful (with the --debug mode), run the following command:
python train.py --debug --no-track
To log the training process, edit the wandb section in config.yaml and remove --no-track from the command line. The config.yaml file contains various configuration settings for the project.
This baseline comes with four different models under the agent_zoo directory: neurips23_start_kit, yaofeng, takeru, and hybrid. You can use any of these models by specifying the -a argument.
python train.py -a hybrid
You can also create your own policy by creating a new module under the agent_zoo directory, which should contain Policy, Recurrent, and RewardWrapper classes.
The training script supports automatic curriculum learning using the Syllabus library. To use it, add --syllabus to the command line.
python train.py --syllabus
The policies directory contains a set of trained policies. For your models, create a directory and copy the checkpoint files to it. To generate a replay, run the following command:
python train.py -m replay -p policies
The replay file ends with .replay.lzma. You can view the replay using the web viewer.
The evaluation script supports the pvp and pve modes. The pve mode spawns all agents using only one policy. The pvp mode spawns groups of agents, each controlled by a different policy.
To evaluate models in the policies directory, run the following command:
python evaluate.py policies pvp -r 10
This generates 10 results json files in the same directory (by using -r 10), each of which contains the results from 200 episodes. Then the task completion metrics can be viewed using:
python analysis/proc_eval_result.py policies