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Official code for ICLR 2024 paper, SEABO: A Simple Search-Based Method for Offline Imitation Learning

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# SEABO: A Simple Search-Based Method for Offline Imitation Learning (ICLR 2024)

SEABO is a simple yet effective method for assigning rewards to unlabeled offline datasets. Our key idea is that if a transition lie close to the expert trajectory, it ought to have a larger reward and vice versa. The illustration of our method can be found below.

![SEABO pipeline](https://raw.githubusercontent.com/dmksjfl/SEABO/master/seabo.png)

## How to run

For reproducing our reported results in the paper, please check the following instructions.

For IQL+SEABO, on MuJoCo tasks, run
```
CUDA_VISIBLE_DEVICES=0 nohup python main_iql.py --env halfcheetah-medium-expert-v2 --seed 5 > out.log 2>&1 &
```

For IQL+SEABO, on AntMaze tasks, run
```
CUDA_VISIBLE_DEVICES=0 nohup python main_iql.py --env antmaze-medium-diverse-v0 --seed 5 --beta 5.0 --temperature 10.0 --expectile 0.9 > out.log 2>&1 &
```

For IQL+SEABO, on Adroit tasks, run
```
CUDA_VISIBLE_DEVICES=0 nohup python main_iql.py --env pen-human-v0 --seed 1 --no_action_dim --temperature 0.5 --dropout_rate 0.1 > out.log 2>&1 &
```

For TD3_BC+SEABO, on MuJoCo tasks, run
```
CUDA_VISIBLE_DEVICES=0 nohup python main.py --env halfcheetah-medium-expert-v2 --seed 1 --dir logs/halfcheetah-medium-expert-v2/ > out.log 2>&1 &
```

## Key flags

```
python main.py \
       --k 1 \
       # how many neighbors are used
       --beta 0.5 \
       # the weighting coefficient
       --scale 1 \
       # the reward scale
       --mode 'sas' \
       # what to query, contain actions or not
       --no_action_dim \
       # whether to include action dimension in the reward calculation
```

## Citation

If you use our method or code in your research, please consider citing the paper as follows:
```
@inproceedings{lyu2024seaboasimple,
 title={SEABO: A Simple Search-Based Method for Offline Imitation Learning},
 author={Jiafei Lyu and Xiaoteng Ma and Le Wan and Runze Liu and Xiu Li and and Zongqing Lu},
 booktitle={International Conference on Learning Representations},
 year={2024}
}
```

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