Jacques' Guide for Installation:

1. Install required packages:

   sudo apt install libopenmpi-dev libosmesa6-dev libgl1-mesa-glx libglfw3 libglew-dev patchelf
   

2. Clone this repository, create a conda environment and install the Spinning Up package:

   git clone https://github.com/JacquesCloete/spinningup.git
   cd spinningup
   conda env create -f conda-spinningup.yml
   conda activate spinningup
   pip install -e .
   

3. Install the MuJoCo v2.1 binaries for Linux, extract the tar file, and move the extracted mujoco210 directory into ~/.mujoco/ (note you have to create this hidden folder yourself).

4. Run sudo nano ~/.bashrc and append the following lines to the bashrc file:

   export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$HOME/.mujoco/mujoco210/bin
   export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/lib/nvidia
   export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libGLEW.so
   

   (note: remember to source the bashrc file after editing; you'll also need to re-activate the conda environment)

If you want to remove the conda environment, run conda remove -n spinningup --all

If you can't get the MuJoCo stuff working, here is an alternative guide that might help.

I've been seeing Invalid MIT-MAGIC-COOKIE-1 key appear in the terminal output when running any of the Spinning Up python scripts on my Ubuntu 20.04 local machine. Doing things like running xhost +local: do not help. Apparently this bug can happen when setting your NVIDIA driver to use OpenCL and CUDA before installing MPI on your local computer; simply switching to the X.Org driver before switching back again to the NVIDIA driver fixes it (see here). Since the bug seems harmless I don't bother.

Original Text:

Status: Maintenance (expect bug fixes and minor updates)

Welcome to Spinning Up in Deep RL!

This is an educational resource produced by OpenAI that makes it easier to learn about deep reinforcement learning (deep RL).

For the unfamiliar: reinforcement learning (RL) is a machine learning approach for teaching agents how to solve tasks by trial and error. Deep RL refers to the combination of RL with deep learning.

This module contains a variety of helpful resources, including:

• a short introduction to RL terminology, kinds of algorithms, and basic theory,
• an essay about how to grow into an RL research role,
• a curated list of important papers organized by topic,
• a well-documented code repo of short, standalone implementations of key algorithms,
• and a few exercises to serve as warm-ups.

Get started at spinningup.openai.com!

Citing Spinning Up

If you reference or use Spinning Up in your research, please cite:

@article{SpinningUp2018,
    author = {Achiam, Joshua},
    title = {{Spinning Up in Deep Reinforcement Learning}},
    year = {2018}
}