SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning

Installation

• Conda Environment:

  • create an environment with conda create -n serl python=3.10

• Recommended:

  • Assume the machines have the lastest Nvdia drivers and CUDA Versions (either 12.1 or 11.x)

  • Run

    pip install --upgrade pip
    
    pip install -e .

    # CUDA 12 installation
    # Note: wheels only available on linux.
    pip install --upgrade "jax[cuda12_pip]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
    
    # CUDA 11 installation
    # Note: wheels only available on linux.
    pip install --upgrade "jax[cuda11_pip]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
    

• Check here for JAX installation with local CUDA and CUDNN installations,

  • This way can be more complicated.

• For running experiments from vision, please also git clone and pip install -e . this library https://github.com/Leo428/efficientnet-jax. It is forked from https://github.com/rwightman/efficientnet-jax to support learning with pre-trained visual encoders (EfficientNet and MobileNets) in JAX and Flax.

Examples

This folder contains example usages of serl as in the paper.