Continuous Control with Coarse-to-fine Reinforcement Learning

A re-implementation of Coarse-to-fine Q-Network (CQN), a sample-efficient value-based RL algorithm for continuous control, introduced in:

Continuous Control with Coarse-to-fine Reinforcement Learning

Younggyo Seo, Jafar Uruç, Stephen James

Our key idea is to learn RL agents that zoom-into continuous action space in a coarse-to-fine manner, enabling us to train value-based RL agents for continuous control with few discrete actions at each level.

See our project webpage https://younggyo.me/cqn/ for more information.

Instructions for RLBench experiments

Install conda environment:

conda env create -f conda_env.yml
conda activate cqn

Install RLBench and PyRep (latest versions at the date of July 10, 2024 should be used). Follow the guide in original repositories for (1) installing RLBench and PyRep and (2) enabling headless mode. (See README in RLBench & Robobase for information on installing RLBench.)

git clone https://github.com/stepjam/RLBench
git clone https://github.com/stepjam/PyRep
# Install PyRep
cd PyRep
git checkout 8f420be8064b1970aae18a9cfbc978dfb15747ef
pip install .
# Install RLBench
cd RLBench
git checkout b80e51feb3694d9959cb8c0408cd385001b01382
pip install .

Pre-collect demonstrations

cd RLBench/rlbench
CUDA_VISIBLE_DEVICES=0 DISPLAY=:0.0 python dataset_generator.py --save_path=/your/own/directory --image_size 84 84 --renderer opengl3 --episodes_per_task 100 --variations 1 --processes 1 --tasks take_lid_off_saucepan --arm_max_velocity 2.0 --arm_max_acceleration 8.0

Run experiments (CQN):

CUDA_VISIBLE_DEVICES=0 DISPLAY=:0.0 python train_rlbench.py rlbench_task=take_lid_off_saucepan num_demos=100 dataset_root=/your/own/directory

Run baseline experiments (DrQv2+):

CUDA_VISIBLE_DEVICES=0 DISPLAY=:0.0 python train_rlbench_drqv2plus.py rlbench_task=take_lid_off_saucepan num_demos=100 dataset_root=/your/own/directory

Instructions for DMC experiments

Run experiments:

CUDA_VISIBLE_DEVICES=0 python train_dmc.py dmc_task=cartpole_swingup

Warning: CQN is not extensively tested in DMC

Acknowledgements

This repository is based on public implementation of DrQ-v2

Citation

@article{seo2024continuous,
  title={Continuous Control with Coarse-to-fine Reinforcement Learning},
  author={Seo, Younggyo and Uru{\c{c}}, Jafar and James, Stephen},
  journal={arXiv preprint arXiv:2407.07787},
  year={2024}
}