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Adversarial Imitation Learning from Incomplete Demonstrations

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# Action-Guided Adversarial Imitation Learning
<img src="https://travis-ci.org/mingfeisun/agail.svg?branch=master" alt="building"/>

## Algorithm framework

## How to use

### 1. Download expert data 
Download expert data from this [dropbox url](https://www.dropbox.com/s/wu6fddmjk5ojrg6/expert_data.zip?dl=0) or [google drive url](https://drive.google.com/file/d/11TWL1gdqXwz-pDRW_hlPpNFJZBFRkM_5/view?usp=sharing). Unzip and place it in the scripts folder (i.e., *scripts/expert_data/*)

### 2. Running training scripts

This repository consists of two version of AGAIL in folder **scripts**: one for discrete actions and another for continuous actions.

* Discrete action control:

``` python
# run a single process
python3 agail_trpo_cartpole.py --loss_percent 0.25 --seed 0 --algo agail # running agail
python3 agail_trpo_cartpole.py --loss_percent 0.25 --seed 0 --algo state # running state-GAIL
python3 agail_trpo_cartpole.py --loss_percent 0.25 --seed 0 --algo trpo # running TRPO
```
``` bash
# run multiple process, e.g., run agail
sh run_cartpole.sh 
```
(Checkpoints and logs will be written into *checkpoint* and *log_trpo_cartpole* folder)

* Continuous action control:

``` python
# run a single process
python3 agail_trpo.py --loss_percent 0.25 --env_id Hopper --expert_path expert_data/mujoco/stochastic.trpo.Hopper.0.00.npz --algo agail # running agail
python3 agail_trpo.py --loss_percent 0.25 --env_id Hopper --expert_path expert_data/mujoco/stochastic.trpo.Hopper.0.00.npz --algo state # running state-GAIL
python3 agail_trpo.py --loss_percent 0.25 --env_id Hopper --expert_path expert_data/mujoco/stochastic.trpo.Hopper.0.00.npz --algo trpo # running TRPO
```
``` bash
# run multiple process
sh run_mujoco.sh
```
(Checkpoints and logs will be written into *checkpoint* and *log_trpo_mujoco* folder)

### 3. Plotting curves
``` python
python3 plot_curve.py --env_id Hopper --timesteps 5000000
```

## Experiment outcomes
* Overall
<img src="docs/overall.png" alt="effective" width="800px"/>

* Effectiveness
<img src="docs/effective.png" alt="effective" width="800px"/>

* Robustness
<img src="docs/robust.png" alt="robust" width="800px"/>

## Citation & Question
If you use the repository for your research, please cite our work:
> **Adversarial Imitation Learning from Incomplete Demonstrations**
> 
> Mingfei Sun, Xiaojuan Ma
>
> International Joint Conference on Artificial Intelligence (IJCAI 2019)

If you encountered any problems when using the codes, please feel free to contact **Mingfei (mingfei.sun@ust.hk)**. Or you can create an issue in this repo.

Visit [www.mingfeisun.com](www.mingfeisun.com) for more research projects on the relevant topics.

agail

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