https://github.com/ermongroup/metairl
Meta-Inverse Reinforcement Learning with Probabilistic Context Variables
https://github.com/ermongroup/metairl
Last synced: 5 months ago
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Meta-Inverse Reinforcement Learning with Probabilistic Context Variables
- Host: GitHub
- URL: https://github.com/ermongroup/metairl
- Owner: ermongroup
- Created: 2019-10-24T21:57:50.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2023-03-16T01:18:19.000Z (over 2 years ago)
- Last Synced: 2025-03-31T16:11:21.822Z (7 months ago)
- Language: Python
- Size: 12.7 MB
- Stars: 71
- Watchers: 9
- Forks: 8
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
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README
## Meta-Inverse Reinforcement Learning with Probabilistic Context Variables
Lantao Yu*, Tianhe Yu*, Chelsea Finn, Stefano Ermon.
The 33rd Conference on Neural Information Processing Systems. (NeurIPS 2019)
[[Paper]](https://arxiv.org/pdf/1909.09314.pdf) [[Website]](https://sites.google.com/view/pemirl)
### Usage
Requirement: The rllab package used in this project is provided [here](https://github.com/ermongroup/MetaIRL/tree/master/rllab).
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](https://github.com/ermongroup/MetaIRL/tree/master/data_fusion_discrete/maze_wall_meta_irl_imitcoeff-0.01_infocoeff-0.1_mbs-50_bs-16_itr-20_preepoch-1000_entropy-1.0_RandomPol_Rew-2-32/2019_05_14_02_33_17_0), 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
```