Golden
OpenAI Baselines

OpenAI Baselines

A set of high-quality implementations of reinforcement learning algorithms created and shared by the OpenAI non-profit AI research company

OpenAI Baselines is a set of high-quality implementations of reinforcement learning algorithms that're intended to benefit the artificial intelligence research community. They make it easier for researchers to replicate, refine, and identify new ideas that can be explored and developed further. 



The OpenAI Baselines were open-sourced by OpenAI in May, 2017. The initial release included DQN -- a reinforcement learning algorithm that combines Q-Learning and deep neural networks to let reinforcement learning work for complex, high-dimensional environments such as video games and robotics -- as well as three of DQN's variants. 

Purpose

OpenAI's stated goal in open-sourcing the OpenAI Baselines is to ensure that apparent reinforcement learning advances are never due to comparisons with buggy or untuned versions of existing algorithms. This is important because reinforcement learning algorithms are highly complex, making results tricky to reproduce. Providing the entire research community with known-good implementations and best practices for creating them is intended to save time implementing pre-existing algorithms so that more time can be spent designing new ones.





Timeline

People

Name
Role
Related Golden topics







Further reading

Title
Author
Link
Type
Date

OpenAI Baselines: DQN

Syzmon Sidor & John Schulman

Web



Documentaries, videos and podcasts

Title
Date
Link

Walker2d training with OpenAI baselines

July 31st, 2017

Companies

Company
CEO
Location
Products/Services

OpenAI

Sam Altman (Chairman)

San Francisco

AI research

References