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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.