/c-swm

Code for ICLR 2020 submission "Contrastive Learning of Structured World Models"

Primary LanguagePython

Contrastive Learning of Structured World Models

NOTICE

  • this code is a forked version and has some changes compared with the official version.

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