This repository is the official implementation of paper Learning Multi-Objective Curricula for Robotic Policy Learning
To install requirements:
pip install -r requirements.txt
To install depdencies, we recommend to use a conda virtual environment.
conda create --name moc python=3.7
To train the model(s) in the paper, run this command:
python train_causal_world.py --task-name <task name> --total-times 10000000 --num-of-envs 5 --device <cpu/cuda>
Training parameters
| Parameters | Meaning |
|---|---|
| seed-num | The number of seed |
| num-of-env | The number of processes used for training |
| task-name | The name of causalworld tasks |
| total-times | Total training timesteps. (We use 10000000 steps in our paper) |
| device | Using CPU or GPU.(option: cpu or cuda) |
Causalworld tasks options:
- picking
- pushing
- pick_and_place
- stacking2
- reaching
Our model achieves the following performance on CausalWorld:
| Model name | Reaching | Picking | Pick_and_place | Pushing | Stacking2 |
|---|---|---|---|---|---|
| MOC DRL (With memory) | 1235.79+/-(48.67) | -4.27+/-(0.27) | 19.32+/-(2.64) | 2.25+/-(2.14) | 10.47+/-(0.36) |
| MOC DRL (Without memory) | 1101.49+/-(32.92) | -4.30+/-(0.30) | -0.98+/-(0.16) | -2.53+/-(0.09) | -5.33+/-(0.21) |
| Independent Curriculum | 1084.76+/-(25.64) | -5.37+/-(0.09) | -2.98+/-(0.71) | -3.67+/-(0.07) | -7.52+/-(0.36) |
| Memory-only | 828.17+/-(17.64) | -13.39+/-(1.78) | -3.37+/-(0.06) | -4.58+/-(0.51) | -7.36+/-(0.47) |
| One Curriculum | 865.00+/-(77.06) | -7.61+/-(0.19) | -5.20+/-(0.49) | -3.67+/-(0.46) | -6.99+/-(0.28) |
| PPO | 724.06+/-(5.03) | -8.32+/-(0.21) | -6.64+/-(0.15) | -6.64+/-(0.17) | -8.78+/-(0.04) |
The column shows the mean episode rewards plus-minus standard deviation at final stage.
@article{corl:jikun22,
author = {Jikun Kang and
Miao Liu and
Abhinav Gupta and
Chris Pal and
Xue Liu and
Jie Fu},
title = {Learning Multi-Objective Curricula for Robotic Policy Learning},
journal = {CoRL},
volume = {abs/2110.03032},
year = {2022}
}
MIT License
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