Lantao Yu*, Tianhe Yu*, Chelsea Finn, Stefano Ermon.
The 33rd Conference on Neural Information Processing Systems. (NeurIPS 2019)
[Paper] [Website]
Requirement: The rllab package used in this project is provided here.
To get expert trajectories for downstream tasks:
python scripts/maze_data_collect.py
After getting expert trajectories, run Meta-Inverse RL to learn context dependent reward functions:
python scripts/maze_wall_meta_irl.py
We provided a pretrained IRL model here, which will be loaded by the following codes by default.
To visualize the context-dependent reward function (Figure 2 in the paper):
python scripts/maze_visualize_reward.py
To use the context-dependent reward function to train a new policy under new dynamics:
python scripts/maze_wall_meta_irl_test.py