multiagent-programmatic-supervision

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multiagent-programmatic-supervision

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PyTorch implementation of Generating Multi-Agent Trajectories using Programmatic Weak Supervision

multiagent-programmatic-supervision 최신버젼 다운로드

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// repository documentation

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# Generating Multi-Agent Trajectories using Programmatic Weak Supervision

Code for paper titled [Generating Multi-Agent Trajectories using Programmatic Weak Supervision](https://arxiv.org/abs/1803.07612) by Zhan et al., ICLR 2019.

## Installation & Setup

Code is written using [PyTorch](https://pytorch.org/) version `1.0.0`.

After cloning the repository, you need to download the data.

**[Update 11/25/20]** The basketball dataset is now available on [AWS Data Exchange](https://aws.amazon.com/marketplace/pp/prodview-7kigo63d3iln2?qid=1606330770194&sr=0-1&ref_=srh_res_product_title#offers). Please make sure to acknowledge Stats Perform if you use the data for your research.

The Boids dataset can be generated by running:
```
$ python datasets/boids/generate_data.py
```
This may take a while, so a pre-generated Boids dataset is included [here](https://drive.google.com/drive/folders/1Qj8PSHoZx-hYMT3scBmq74WbE9BF4hm8?usp=sharing).

## Running the Code

To train a model, you can edit the parameters in `train_model.sh` and run the script from the command-line:
```
$ ./train_model.sh
```
After training a model,
```
$ python sample.py -t <trial_id> -n <num_samples> -b <burn_in> --run --plot
```
will generate and plot samples from a model and save them in `saved/<trial_id>/experiments/sample/`.

For full usage, use flag `--help`.

### Scripts

To see the parameters of a past experiment (for reproducability), run:
```
$ python scripts/print_params.py -t <trial_id>
```
To visualize examples from a test dataset, run:
```
$ python scripts/show_groundtruth.py -d <dataset> -n <num_examples>
```
which will save them into `datasets/<dataset>/data/examples/`.

To compute and compare domain statistics for basketball, run:
```
$ python sample.py -t <trial_id> -n 1000 -b 10 --run
$ python scripts/compute_bball_stats.py -t <trial_id>
```

## Pretrained Models

Included in this repository in `saved/` are four pretrained models for basketball as discussed in the paper:

| Trial ID |    Model    |
|:--------:|:-----------:|
|    101   |  RNN_GAUSS  |
|    102   | VRNN_SINGLE |
|    103   |  VRNN_INDEP |
|    104   |  MACRO_VRNN |

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