Awesome
Achievement Distillation
This is the code for the paper Discovering Hierarchical Achievements in Reinforcement Learning via Contrastive Learning accepted to NeurIPS 2023.
<img src="figures/overview.png" width="800"> <img src="figures/result.png" width="600">Installation
conda create --name ad-crafter python=3.10
conda activate ad-crafter
pip install --upgrade "setuptools==65.7.0" "wheel==0.38.4"
pip install -r requirements.txt
pip install -e .
Training
If you are working in a Bash environment, you can use the following commands. The system will set the timestamp to debug by default.
PPO
python train.py --exp_name ppo --log_stats --save_ckpt
PPO + Achievement Distillation (ours)
python train.py --exp_name ppo_ad --log_stats --save_ckpt
If you are working in an environment that utilizes the Slurm Workload Manager, you can submit your job using submit.py. In this setup, the system automatically assigns a timestamp that corresponds to the actual start time of your job.
Evaluation
If you want to evaluate an agent on a new environment, you can use the following command. This will create a video displaying the agent's behavior.
python eval.py --exp_name [your exp name] --timestamp [your timestamp]
Citation
If you find this code useful, please cite this work.
@inproceedings{moon2023ad,
title={Discovering Hierarchical Achievements in Reinforcement Learning via Contrastive Learning},
author={Seungyong Moon and Junyoung Yeom and Bumsoo Park and Hyun Oh Song},
booktitle={Neural Information Processing Systems},
year={2023}
}