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Implementation of Robust Imitation Learning against Variations in Environment Dynamics

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# Robust Imitation learning with Multiple perturbed Environments (RIME)
This repository is an implementation of [Robust Imitation Learning against Variations in Environment Dynamics](https://arxiv.org/abs/2206.09314) published to ICML 2022.

The RIME code are modified from the codes of [pytorch-a2c-ppo-acktr](https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail).

## Supported Environments
+ [MuJoCo](https://www.roboti.us/index.html) (via [OpenAI Gym](https://www.gymlibrary.ml/))

For perturbed tasks, I only used `mujoco200` and made these MuJoCo tasks with perturbed dynamics by changing components in xml files for the tasks to introduce fixed dynamics perturbations. For more details, please go to the `environments` folder.

## Requirements
I provide all libraries and packages for this codes.
```
pip install -r requirements.txt
```

## Run Example 
For training agents (over 10 random seeds), we can change `env-parameter` & `algo-name` for selecting other dynamics perturbation type (for single dynamics parameter cases) or training other algorithms as follows:
+ env-parameter: gravity / mass
+ algo-name: RIME / RIME+WSD / OMME / GAIL-Mixture / GAIL-Single
```
# train the agent in the 2 sampled interaction environments setting
python main.py --env-name=Hopper-v2 --env-parameter=gravity --sampled-envs=2 --algo-name=RIME+WSD

# train the agent in the 3 sampled interaction environments setting
python main.py --env-name=Hopper-v2 --env-parameter=gravity --sampled-envs=3 --algo-name=RIME+WSD

# train the agent in the 4 sampled interaction environments setting (2-dim perturbation parameter case)
python main.py --env-name=Hopper-v2 --sampled-envs=4 --algo-name=RIME+WSD
```

RIME

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