Custom features in this repository

• GPU support for Pytorch implementation of SAC and TD3 algorithms
• TF2 implementation of SAC and TD3 algorithms (not completely tested)
• Easy addition of new Mujoco environment

Troubleshooting when building the conda virtual environment from runnable.yml

• If mpi4py is missing or unable to install it by pip, try:

1. sudo apt-get update -y
2. sudo apt-get install -y python3-mpi4py
3. pip install mpi4py

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}
}