liuqi8827 / relod

An efficient remote-onboard architecture for real-time Reinforcement Learning

ReLoD: The Remote-Local Distributed System for Real-time Reinforcement Learning on Vision-Based Robotics Tasks

ReLoD uses a wired local and a wireless remote computer to perform real-time learning, an appealing setting for industrial learning systems. It is a generalist RL system for learning with real robots from scratch! Check out how ReLoD learns to perform vision-based tasks on UR5 and Roomba (iRobot Create 2):

Supported Algorithms

• Soft Actor Critic (SAC)
• Proximal Policy Optimization (PPO)

N.B: All vision-based experiments use Random Augmented Data (RAD) to improve sample efficiency

Supported Tasks

• UR5-VisualReacher
• Create-Reacher
• Vector-ChargerDetector

Installation instructions

1. Download Mujoco and license files to ~/.mujoco
2. Install miniconda or anaconda
3. Create a virtual environment:

conda create --name myenv python=3.6    # Python 3.6 is necessary
conda activate myenv

3. Add the following to ~/.bashrc:

conda activate myenv
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/<username>/.mujoco/mjpro210/bin   # Change based on mujoco version
export MUJOCO_GL="egl"  # System specific

and run:

source ~/.bashrc

4. Install packages with:

pip install -r requirements.txt
pip install .

Cite

• Wang, Y., Vasan, G., & Mahmood, A. R. (2022). Real-time reinforcement learning for vision-based robotics utilizing local and remote computers. arXiv preprint arXiv:2210.02317.