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Learning separated models and imaging for visual imitation learning (SeMAIL)

The official implementation of SeMAIL.

Method

SeMAIL SeMAIL

Example usage

conda env create -f SeMAIL.yaml
conda activate SeMAIL

To train agents for each environmnet download the expert data from the provided link and run:

# car racing
python -u SeMAIL.py --expert_dir ../data/car_racing_expert --steps 500000 --seed 2021 --image_size 64 --batch_size 64 --task gym_car_racing_none-none --video_datadir ../driving_car_16/driving_car_16 --log_dir logdir/SeMAIL  --disen_rec_scale 1.0
# locomotion tasks
python -u SeMAIL.py --expert_dir ../data/walker_run_expert --steps 1000000 --seed 2021 --image_size 64 --batch_size 64 --task dmc_walker_run_driving-driving --video_datadir ../driving_car_16/driving_car_16 --log_dir logdir/SeMAIL  --disen_rec_scale 0.25

To train agents for each environmnet from expert demonstration data with different video backgrounds, run:

python -u SeMAIL.py --expert_dir ../data/walker_run_expert-8 --steps 1000000 --seed 2021 --image_size 64 --batch_size 64 --task dmc_walker_run_driving-driving --video_datadir ../driving_car_16/8 --log_dir logdir/SeMAIL-8  --disen_rec_scale 0.25

The training plots and output visualizations will be in the log folder:

tensorboard --logdir logdir

Acknowledgement

We appreciate the following github repos where we borrow code from:

https://github.com/rmrafailov/VMAIL

https://github.com/kyonofx/tia

semail's People

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