Generative Multi-Agent Behavioral Cloning (https://arxiv.org/abs/1803.07612)
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bball_data
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LICENSE.md
README.md
model.py
model_utils.py
plot.py
sample.py
train.py
train_model.sh

README.md

Generative Multi-Agent Behavioral Cloning

Updates!!

5/20/2018 - a new version of the code will be available soon (with PyTorch v0.4), stay tuned!

Code

Code is written with PyTorch v0.2 (Python 3.6.1).

Dataset is available from STATS. A pre-processed version is available here. Download the data into the bball_data/data/ folder.

Example Run

You can edit train_model.sh and run that script, or run train.py directly with command-line parameters.

$ ./train_model.py
$ python sample.py -t 105 -b 10 -n 10
$ python plot.py -t 105 --animate

Trained models for RNN_GAUSS (101), VRNN_SINGLE (102), VRNN_INDEP (103), and our model MACRO_VRNN (104) are included.

Files

model.py contains the models. MACRO_VRNN is our model with macro-goals.

train.py contains the training process, and can be called with train_model.sh.

sample.py is used to sample rollouts from a trained model.

plot.py is used to plot the samples as well as animate them (with --animate flag). --showmacro will display macro-goals, where applicable.

model_utils.py contains functions for sampling and calculating various losses.

bball_data/__init__.py contains the Dataset object.

bball_data/cfg.py contains constants for the data.

bball_data/macro_goals.py is the script used to extract macro-goals. Don't need to run again.

bball_data/utils.py contains the functions for plotting and animating.