Contrastive Learning of Structured World Models

Last update: Jan 06, 2023

Related tags

Overview

Contrastive Learning of Structured World Models

This repository contains the official PyTorch implementation of:

Contrastive Learning of Structured World Models.
Thomas Kipf, Elise van der Pol, Max Welling.
http://arxiv.org/abs/1911.12247

Abstract: A structured understanding of our world in terms of objects, relations, and hierarchies is an important component of human cognition. Learning such a structured world model from raw sensory data remains a challenge. As a step towards this goal, we introduce Contrastively-trained Structured World Models (C-SWMs). C-SWMs utilize a contrastive approach for representation learning in environments with compositional structure. We structure each state embedding as a set of object representations and their relations, modeled by a graph neural network. This allows objects to be discovered from raw pixel observations without direct supervision as part of the learning process. We evaluate C-SWMs on compositional environments involving multiple interacting objects that can be manipulated independently by an agent, simple Atari games, and a multi-object physics simulation. Our experiments demonstrate that C-SWMs can overcome limitations of models based on pixel reconstruction and outperform typical representatives of this model class in highly structured environments, while learning interpretable object-based representations.

Requirements

Python 3.6 or 3.7
PyTorch version 1.2
OpenAI Gym version: 0.12.0 pip install gym==0.12.0
OpenAI Atari_py version: 0.1.4: pip install atari-py==0.1.4
Scikit-image version 0.15.0 pip install scikit-image==0.15.0
Matplotlib version 3.0.2 pip install matplotlib==3.0.2

Generate datasets

2D Shapes:

python data_gen/env.py --env_id ShapesTrain-v0 --fname data/shapes_train.h5 --num_episodes 1000 --seed 1
python data_gen/env.py --env_id ShapesEval-v0 --fname data/shapes_eval.h5 --num_episodes 10000 --seed 2

3D Cubes:

python data_gen/env.py --env_id CubesTrain-v0 --fname data/cubes_train.h5 --num_episodes 1000 --seed 3
python data_gen/env.py --env_id CubesEval-v0 --fname data/cubes_eval.h5 --num_episodes 10000 --seed 4

Atari Pong:

python data_gen/env.py --env_id PongDeterministic-v4 --fname data/pong_train.h5 --num_episodes 1000 --atari --seed 1
python data_gen/env.py --env_id PongDeterministic-v4 --fname data/pong_eval.h5 --num_episodes 100 --atari --seed 2

Space Invaders:

python data_gen/env.py --env_id SpaceInvadersDeterministic-v4 --fname data/spaceinvaders_train.h5 --num_episodes 1000 --atari --seed 1
python data_gen/env.py --env_id SpaceInvadersDeterministic-v4 --fname data/spaceinvaders_eval.h5 --num_episodes 100 --atari --seed 2

3-Body Gravitational Physics:

python data_gen/physics.py --num-episodes 5000 --fname data/balls_train.h5 --seed 1
python data_gen/physics.py --num-episodes 1000 --fname data/balls_eval.h5 --eval --seed 2

Run model training and evaluation

2D Shapes:

python train.py --dataset data/shapes_train.h5 --encoder small --name shapes
python eval.py --dataset data/shapes_eval.h5 --save-folder checkpoints/shapes --num-steps 1

3D Cubes:

python train.py --dataset data/cubes_train.h5 --encoder large --name cubes
python eval.py --dataset data/cubes_eval.h5 --save-folder checkpoints/cubes --num-steps 1

Atari Pong:

python train.py --dataset data/pong_train.h5 --encoder medium --embedding-dim 4 --action-dim 6 --num-objects 3 --copy-action --epochs 200 --name pong
python eval.py --dataset data/pong_eval.h5 --save-folder checkpoints/pong --num-steps 1

Space Invaders:

python train.py --dataset data/spaceinvaders_train.h5 --encoder medium --embedding-dim 4 --action-dim 6 --num-objects 3 --copy-action --epochs 200 --name spaceinvaders
python eval.py --dataset data/spaceinvaders_eval.h5 --save-folder checkpoints/spaceinvaders --num-steps 1

3-Body Gravitational Physics:

python train.py --dataset data/balls_train.h5 --encoder medium --embedding-dim 4 --num-objects 3 --ignore-action --name balls
python eval.py --dataset data/balls_eval.h5 --save-folder checkpoints/balls --num-steps 1

Cite

If you make use of this code in your own work, please cite our paper:

@article{kipf2019contrastive,
  title={Contrastive Learning of Structured World Models}, 
  author={Kipf, Thomas and van der Pol, Elise and Welling, Max}, 
  journal={arXiv preprint arXiv:1911.12247}, 
  year={2019} 
}

Contrastive Learning of Structured World Models

Related tags

Overview

Contrastive Learning of Structured World Models

Requirements

Generate datasets

Run model training and evaluation

Cite

Owner

Thomas Kipf

The official repository for "Score Transformer: Generating Musical Scores from Note-level Representation" (MMAsia '21)

DP-CL(Continual Learning with Differential Privacy)

This code provides a PyTorch implementation for OTTER (Optimal Transport distillation for Efficient zero-shot Recognition), as described in the paper.

[Preprint] "Chasing Sparsity in Vision Transformers: An End-to-End Exploration" by Tianlong Chen, Yu Cheng, Zhe Gan, Lu Yuan, Lei Zhang, Zhangyang Wang

Code release for SLIP Self-supervision meets Language-Image Pre-training

Deep Residual Learning for Image Recognition

NIMA: Neural IMage Assessment

This project uses reinforcement learning on stock market and agent tries to learn trading. The goal is to check if the agent can learn to read tape. The project is dedicated to hero in life great Jesse Livermore.

This is the implementation of GGHL (A General Gaussian Heatmap Labeling for Arbitrary-Oriented Object Detection)

Pi-NAS: Improving Neural Architecture Search by Reducing Supernet Training Consistency Shift (ICCV 2021)

Joint parameterization and fitting of stroke clusters

Library for time-series-forecasting-as-a-service.

Human head pose estimation using Keras over TensorFlow.

Jaxtorch (a jax nn library)

A really easy-to-use and powerful sudoku solver.

Memory-efficient optimum einsum using opt_einsum planning and PyTorch kernels.

Repository for the paper "Online Domain Adaptation for Occupancy Mapping", RSS 2020

Image Captioning using CNN ,LSTM and Attention

Example of a Quantum LSTM

A (PyTorch) imbalanced dataset sampler for oversampling low frequent classes and undersampling high frequent ones.