IMMA

KO | EN

gitlite — search

#javascript #python #hacktoberfest #react #ai #typescript #llm #go #golang #android #machine-learning #rust #deep-learning #linux

(★ 37)

Official code for "Interaction Modeling with Multiplex Attention" (NeurIPS 2022)

IMMA 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Interaction Modeling with Multiplex Attention
Authors: [Fan-Yun Sun](https://cs.stanford.edu/~sunfanyun/), [Isaac Kauvar](https://ikauvar.github.io/), [Ruohan Zhang](https://ai.stanford.edu/~zharu/), [Jiachen Li](https://jiachenli94.github.io/), [Mykel Kochenderfer](https://mykel.kochenderfer.com/), [Jiajun Wu](https://jiajunwu.com/), [Nick Haber](https://ed.stanford.edu/faculty/nhaber)

Abstract: *Modeling multi-agent systems requires understanding how agents interact. Such systems are often difficult to model because they can involve a variety of types of interactions that layer together to drive rich social behavioral dynamics. Here we introduce a method for accurately modeling multi-agent systems. We present Interaction Modeling with Multiplex Attention (IMMA), a forward prediction model that uses a multiplex latent graph to represent multiple independent types of interactions and attention to account for relations of different strengths. We also introduce Progressive Layer Training, a training strategy for this architecture. We show that our approach outperforms state-of-the-art models in trajectory forecasting and relation inference, spanning three multi-agent scenarios: social navigation, cooperative task achievement, and team sports. We further demonstrate that our approach can improve zero-shot generalization and allows us to probe how different interactions impact agent behavior.*

This repository contains the codes for our paper, which is accepted at NeurIPs 2022. 
For more details, please refer to the paper ([arxiv](https://arxiv.org/abs/2208.10660), [openreview](https://openreview.net/forum?id=SeHslYhFx5-)).

## Environment Setup
1. Install [Python-RVO2](https://github.com/sybrenstuvel/Python-RVO2) library
2. Install [socialforce](https://github.com/ChanganVR/socialforce) library
3. Install necessary packages with pip
```
pip install -r requirements.txt
```

## Data Setup

#### Social Navigation Environment ####
This is a simulated environment inspired by
[https://github.com/vita-epfl/CrowdNav](https://github.com/vita-epfl/CrowdNav).
After installing necessary dependencies, refer to the following sample commands to start the simulation.
```.bash
randomseed=17
dataset_size=100000
obs_frames=24
rollouts=10

cd data_utils/socialnav
python generate_dataset.py --dataset_size $dataset_size \
                           --randomseed $randomseed \
                           --obs_frames ${obs_frames} \
                           --rollouts ${rollouts}
```
The resulting dataset will be stored at `datasets/*.tensor`. You can make modifications to the config file `dat_utils/socialnav/configs/default.py` to change the simulation setting.

To inspect and interact with the environment (control the embodied agent with
your arrow keys):
```.bash
cd data_utils/socialnav
python human_play.py
```

#### PHASE ####
The preprocessed dataset is under `datasets/phase/collab`.
To load the dataset, refer to the function *prepare_dataset* in `data_utils/load_dataset.py`.

#### NBA dataset ####
Download the preprocessed dataset [here](https://drive.google.com/file/d/1eJbDHy3fOHfzOStf-jSuYCz_YQloQU3s/view?usp=sharing) (or run `gdown 1eJbDHy3fOHfzOStf-jSuYCz_YQloQU3s`) and place it under `datasets`. 
Alternatively, you can create your own dataset from raw sportVU logs (refer to [this repository](https://github.com/linouk23/NBA-Player-Movements) or the code under `data_utils/bball`)
To load the dataset, refer to the function *prepare_dataset* in `data_utils/load_dataset.py`.

## Training and Evaluation
Find sample commands at `run_socialnav.sh`, `run_phase.sh` and `run_bball.sh`.

## Progressive Layered Training
Loss curve visualized over the course of training IMMA with PLT on the NBA dataset. New layers are added after the model
"converges".
![Teaser image](https://raw.githubusercontent.com/sunfanyunn/IMMA/main/plt.png)

## Citation
If you find the code or paper useful for your research, please cite our paper:
```bibtex
@article{sun2022interaction,
  title={Interaction Modeling with Multiplex Attention},
  author={Sun, Fan-Yun and Kauvar, Isaac and Zhang, Ruohan and Li, Jiachen and Kochenderfer, Mykel and Wu, Jiajun and Haber, Nick},
  journal={Advances in Neural Information Processing Systems},
  year={2022}
}
```

## Acknwledgement 
In htis project we use (parts of) the implementations from the following works:

- [nri](https://github.com/ethanfetaya/NRI)
- [RelationalGraphLearning](https://github.com/ChanganVR/RelationalGraphLearning)

We thank the authors for open sourcing their methods.

IMMA

IMMA 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

IMMA

IMMA 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README