Decentralized Policy Learning with Partial Observation and Mechanical Constraints

This is the python code for Decentralized Policy Learning with Partial Observation and Mechanical Constraints for Multi-person Modeling.

Author

Keisuke Fujii - https://sites.google.com/site/keisuke1986en/

Reference

Keisuke Fujii, Naoya Takeishi, Yoshinobu Kawahara,Kazuya Takeda, "Decentralized Policy Learning with Partial Observation and Mechanical Constraints for Multi-person Modeling", Neural Networks, 171, 40-52, 2024 (arXiv: https://arxiv.org/abs/2007.03155)

Requirements

• python 3.6
• To install requirements:

pip install -r requirements.txt

Usage

• Run run.sh for a simiple demonstration of training and test using the NBA dataset (only one game).

• Actual commands in training and test of our model are also in run.sh (commented).

• Data can be downloaded from https://github.com/rajshah4/BasketballData

Note for using your own data

• There may be trade-offs between training data size and model (and movement) complexity.
• For example, if you have smaller data and the movement is simple, you can use simple model such as RNN.

Pretrained Models

You can download pretrained models from weights/ for NBA dataset as discussed in the paper.

Results

Our model achieves the following performance (Table 2 in the paper):

Model name	position	velocity	acceleration
1. Velocity	1.41 +/- 0.34	1.08 +/- 0.21	10.90 +/- 2.09
2. RNN-Gauss	1.31 +/- 0.32	1.05 +/- 0.13	1.88 +/- 0.30
3. VRNN	0.71 +/- 0.17	0.68 +/- 0.10	1.43 +/- 0.20
4. VRNN-macro	0.71 +/- 0.17	0.68 +/- 0.10	1.43 +/- 0.20
5. VRNN-Mech	0.69 +/- 0.17	0.68 +/- 0.10	1.37 +/- 0.20
6. VRNN-Bi	0.72 +/- 0.19	0.66 +/- 0.10	1.36 +/- 0.19
7. VRNN-macro-Bi-Mech	0.73 +/- 0.18	0.68 +/- 0.10	1.34 +/- 0.19