D2 Imitation

Code repository for the following paper:

Deterministic and Discriminative Imitation (D2-Imitation): Revisiting Adversarial Imitation for Sample Efficiency

Mingfei Sun, Sam Devlin, Katja Hofmann, Shimon Whiteson

AAAI 2022, [pdf].

D2-Imitation algorithm overview

Overall performance

Compared with adversarial methods	Compared with off-policy distribution matching

Overall robustness with different numbers of trajectories

How to use

Building docker image

bash docker_build.sh

Running the code

• Download the demonstration data

cd src
bash download_data-v2.sh # this might take a while

• Run in single thread mode

cd src 
bash docker_shell.sh # start a docker container
python3 -m baselines.imitation.d2_imitation \
    --seed 0  \
    --env_id HalfCheetah-v2  \
    --expert_path traj_configs-v2/halfcheetah-sac-20.txt  \
    --total_timesteps 500000

The experimental results are logged into log folder.

Citation

Please cite our paper if you find this codebase useful:

@article{sun2021deterministic,
  title={Deterministic and Discriminative Imitation (D2-Imitation): Revisiting Adversarial Imitation for Sample Efficiency},
  author={Sun, Mingfei and Devlin, Sam and Hofmann, Katja and Whiteson, Shimon},
  journal={arXiv preprint arXiv:2112.06054},
  year={2021}
}