A look-ahead multi-entity Transformer for modeling coordinated agents.

Last update: Dec 16, 2022

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Overview

`baller2vec++`

This is the repository for the paper:

Michael A. Alcorn and Anh Nguyen. baller2vec++: A Look-Ahead Multi-Entity Transformer For Modeling Coordinated Agents. arXiv. 2021.


To learn statistically dependent agent trajectories, `baller2vec++` uses a specially designed self-attention mask to simultaneously process three different sets of features vectors in a single Transformer. The three sets of feature vectors consist of location feature vectors like those found in `baller2vec`, look-ahead trajectory feature vectors, and starting location feature vectors. This design allows the model to integrate information about concurrent agent trajectories through multiple Transformer layers without seeing the future (in contrast to `baller2vec`).

To learn statistically dependent agent trajectories, baller2vec++ uses a specially designed self-attention mask to simultaneously process three different sets of features vectors in a single Transformer. The three sets of feature vectors consist of location feature vectors like those found in baller2vec, look-ahead trajectory feature vectors, and starting location feature vectors. This design allows the model to integrate information about concurrent agent trajectories through multiple Transformer layers without seeing the future (in contrast to baller2vec).


Training sample	`baller2vec`	`baller2vec++`

When trained on a dataset of perfectly coordinated agent trajectories, the trajectories generated by baller2vec are completely uncoordinated while the trajectories generated by baller2vec++ are perfectly coordinated.


Ground truth	`baller2vec`	`baller2vec`	`baller2vec`

Ground truth	`baller2vec++`	`baller2vec++`	`baller2vec++`

While baller2vec occasionally generates realistic trajectories for the red defender, it also makes egregious errors. In contrast, the trajectories generated by baller2vec++ often seem plausible. The red player was placed last in the player order when generating his trajectory with baller2vec++.

Citation

If you use this code for your own research, please cite:

@article{alcorn2021baller2vec,
   title={\texttt{baller2vec++}: A Look-Ahead Multi-Entity Transformer For Modeling Coordinated Agents},
   author={Alcorn, Michael A. and Nguyen, Anh},
   journal={arXiv preprint arXiv:2104.11980},
   year={2021}
}

Training `baller2vec++`

Setting up `.basketball_profile`

After you've cloned the repository to your desired location, create a file called .basketball_profile in your home directory:

nano ~/.basketball_profile

and copy and paste in the contents of .basketball_profile, replacing each of the variable values with paths relevant to your environment. Next, add the following line to the end of your ~/.bashrc:

source ~/.basketball_profile

and either log out and log back in again or run:

source ~/.bashrc

You should now be able to copy and paste all of the commands in the various instructions sections. For example:

echo ${PROJECT_DIR}

should print the path you set for PROJECT_DIR in .basketball_profile.

Installing the necessary Python packages

cd ${PROJECT_DIR}
pip3 install --upgrade -r requirements.txt

Organizing the play-by-play and tracking data

Copy events.zip (which I acquired from here [mirror here] using https://downgit.github.io) to the DATA_DIR directory and unzip it:

mkdir -p ${DATA_DIR}
cp ${PROJECT_DIR}/events.zip ${DATA_DIR}
cd ${DATA_DIR}
unzip -q events.zip
rm events.zip

Descriptions for the various EVENTMSGTYPEs can be found here (mirror here).

Clone the tracking data from here (mirror here) to the DATA_DIR directory:

cd ${DATA_DIR}
git clone [email protected]:linouk23/NBA-Player-Movements.git

A description of the tracking data can be found here.

Generating the training data

cd ${PROJECT_DIR}
nohup python3 generate_game_numpy_arrays.py > data.log &

You can monitor its progress with:

top

or:

ls -U ${GAMES_DIR} | wc -l

There should be 1,262 NumPy arrays (corresponding to 631 X/y pairs) when finished.

Running the training script

Run (or copy and paste) the following script, editing the variables as appropriate.

#!/usr/bin/env bash

JOB=$(date +%Y%m%d%H%M%S)

echo "train:" >> ${JOB}.yaml
task=basketball  # "basketball" or "toy".
echo "  task: ${task}" >> ${JOB}.yaml
if [[ "$task" = "basketball" ]]
then

    echo "  train_valid_prop: 0.95" >> ${JOB}.yaml
    echo "  train_prop: 0.95" >> ${JOB}.yaml
    echo "  train_samples_per_epoch: 20000" >> ${JOB}.yaml
    echo "  valid_samples: 1000" >> ${JOB}.yaml
    echo "  workers: 10" >> ${JOB}.yaml
    echo "  learning_rate: 1.0e-5" >> ${JOB}.yaml
    echo "  patience: 20" >> ${JOB}.yaml

    echo "dataset:" >> ${JOB}.yaml
    echo "  hz: 5" >> ${JOB}.yaml
    echo "  secs: 4.2" >> ${JOB}.yaml
    echo "  player_traj_n: 11" >> ${JOB}.yaml
    echo "  max_player_move: 4.5" >> ${JOB}.yaml

    echo "model:" >> ${JOB}.yaml
    echo "  embedding_dim: 20" >> ${JOB}.yaml
    echo "  sigmoid: none" >> ${JOB}.yaml
    echo "  mlp_layers: [128, 256, 512]" >> ${JOB}.yaml
    echo "  nhead: 8" >> ${JOB}.yaml
    echo "  dim_feedforward: 2048" >> ${JOB}.yaml
    echo "  num_layers: 6" >> ${JOB}.yaml
    echo "  dropout: 0.0" >> ${JOB}.yaml
    echo "  b2v: False" >> ${JOB}.yaml

else

    echo "  workers: 10" >> ${JOB}.yaml
    echo "  learning_rate: 1.0e-4" >> ${JOB}.yaml

    echo "model:" >> ${JOB}.yaml
    echo "  embedding_dim: 20" >> ${JOB}.yaml
    echo "  sigmoid: none" >> ${JOB}.yaml
    echo "  mlp_layers: [64, 128]" >> ${JOB}.yaml
    echo "  nhead: 4" >> ${JOB}.yaml
    echo "  dim_feedforward: 512" >> ${JOB}.yaml
    echo "  num_layers: 2" >> ${JOB}.yaml
    echo "  dropout: 0.0" >> ${JOB}.yaml
    echo "  b2v: True" >> ${JOB}.yaml

fi

# Save experiment settings.
mkdir -p ${EXPERIMENTS_DIR}/${JOB}
mv ${JOB}.yaml ${EXPERIMENTS_DIR}/${JOB}/

gpu=0
cd ${PROJECT_DIR}
nohup python3 train_baller2vecplusplus.py ${JOB} ${gpu} > ${EXPERIMENTS_DIR}/${JOB}/train.log &

A look-ahead multi-entity Transformer for modeling coordinated agents.

Related tags

Overview

`baller2vec++`

Citation

Training `baller2vec++`

Setting up `.basketball_profile`

Installing the necessary Python packages

Organizing the play-by-play and tracking data

Generating the training data

Running the training script

Owner

Michael A. Alcorn

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A look-ahead multi-entity Transformer for modeling coordinated agents.

Related tags

Overview

baller2vec++

Citation

Training baller2vec++

Setting up .basketball_profile

Installing the necessary Python packages

Organizing the play-by-play and tracking data

Generating the training data

Running the training script

Owner

Michael A. Alcorn

Text-Summarization-using-NLP - Text Summarization using NLP to fetch BBC News Article and summarize its text and also it includes custom article Summarization

Extract Keywords from sentence or Replace keywords in sentences.

ANTLR (ANother Tool for Language Recognition) is a powerful parser generator for reading, processing, executing, or translating structured text or binary files.

Natural Language Processing at EDHEC, 2022

Unet-TTS: Improving Unseen Speaker and Style Transfer in One-shot Voice Cloning

HAIS_2GNN: 3D Visual Grounding with Graph and Attention

TunBERT is the first release of a pre-trained BERT model for the Tunisian dialect using a Tunisian Common-Crawl-based dataset.

Translate U is capable of translating the text present in an image from one language to the other.

translate using your voice

An official repository for tutorials of Probabilistic Modelling and Reasoning (2021/2022) - a University of Edinburgh master's course.

Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.

Code for the paper "BERT Loses Patience: Fast and Robust Inference with Early Exit".

A python project made to generate code using either OpenAI's codex or GPT-J (Although not as good as codex)

초성 해석기 based on ko-BART

apple's universal binaries BUT MUCH WORSE (PRACTICAL SHITPOST) (NOT PRODUCTION READY)

中文問句產生器；使用台達電閱讀理解資料集(DRCD)

customer care chatbot made with Rasa Open Source.

Text classification on IMDB dataset using Keras and Bi-LSTM network

Incorporating KenLM language model with HuggingFace implementation of Wav2Vec2CTC Model using beam search decoding

A combination of autoregressors and autoencoders using XLNet for sentiment analysis

`baller2vec++`

Training `baller2vec++`

Setting up `.basketball_profile`