LECO: Learnable Episodic Count (NeurIPS 2022)

This is an official implementation of LECO: Learnable Episodic Count for Task-Specific Intrinsic Reward [arxiv]

This repo is extended from the original Sample Factory by Aleksei Petrenko et al.

Requirements

• torch==1.9.0
• gym-minigrid==1.0.3
• deepmind-lab @ file:///tmp/dmlab_pkg/deepmind_lab-1.0-py3-none-any.whl

Training Scripts

Example command for DMLab task

Two nodes with 4 V100 gpus on each node

python -m dist.launch --nnodes=2 --node_rank=0 --nproc_per_node=4 --master_addr=$MASTER_ADDR -m sample_factory.algorithms.appo.train_appo --cfg=lstm_dmlab_single_leco --train_dir=/your/train/directory --experiment=your_experiment_name
python -m dist.launch --nnodes=2 --node_rank=1 --nproc_per_node=4 --master_addr=$MASTER_ADDR -m sample_factory.algorithms.appo.train_appo --cfg=lstm_dmlab_single_leco --train_dir=/your/train/directory --experiment=your_experiment_name

Example command for MiniGrid task

Single node with 2 V100 gpus

python -m dist.launch --nnodes=1 --node_rank=0 --nproc_per_node=2 --master_addr=$MASTER_ADDR -m sample_factory.algorithms.appo.train_appo --cfg=lstm_MiniGrid-ObstructedMaze-Full_leco --train_dir=/your/train/directory --experiment=your_experiment_name

Citation

@inproceedings{jo2022leco,
 author = {Jo, Daejin and Kim, Sungwoong  and Nam, Daniel and Kwon, Taehwan and Rho, Seungeun and Kim, Jongmin and Lee, Donghoon},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh},
 pages = {30432--30445},
 publisher = {Curran Associates, Inc.},
 title = {LECO: Learnable Episodic Count for Task-Specific Intrinsic Reward},
 url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/c43b2989b1ba055aa713a4abbe4a8b05-Paper-Conference.pdf},
 volume = {35},
 year = {2022}
}

Contact

Daejin Jo, [email protected]
Daniel Wontae Nam, [email protected]