LESSON: Learning to Integrate Exploration Strategies for Reinforcement Learning via an Option Framework

This is the code for the paper LESSON accepted at ICML'2023. The paper can be found at [paper].

LESSON Architecture:

Installation

git clone https://github.com/beanie00/LESSON.git
conda create -n lesson
conda activate lesson
pip3 install -r requirements.txt

Training

python3 train.py [**arguments]

arguments

• --env : Choose minigrid environment to learn from. A list of environments can be found in https://minigrid.farama.org/environments/minigrid/
• --algorithm : Choose between DQN and DRQN (default : DQN)
• --seed : Select seed value
• --rnd_scale : intrinsic reward coefficient alpha
• --log_wandb : Log using wandb

example

python3 train.py --env MiniGrid-LavaCrossingS9N1-v0 --rnd_scale 0 --algorithm dqn --seed 42 --log_wandb True

Acknowledgements

We referred to https://github.com/schalappe/Deep-Double-Q-Learning-for-Minigrid while implementing DQN in the MiniGrid environment in our LESSON implementation.

References

If you find our code helpful for your research, please consider citing the paper!

@inproceedings{kim2023lesson,
  title={LESSON: Learning to Integrate Exploration Strategies for Reinforcement Learning via an Option Framework},
  author={Kim, Woojun and Kim, Jeonghye and Sung, Youngchul},
  booktitle={International Conference on Machine Learning},
  pages={16619--16638},
  year={2023},
  organization={PMLR}
}