Modified by: Rami Sketcher
Added Algorithms:
- (MPC-SAC) Model Predictive-Actor Critic Reinforcement Learning for Dexterous Manipulation (IEEEXplore) (Ours)
- (MEMB) Model Embedding Model-Based RL (Arxiv)
- (MEMB-PE) Model Embedding Model-Based RL + Probabilistic Ensemble (Ours)
Status: Maintenance (expect bug fixes and minor updates)
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!
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}
}