UC Berkeley Researchers Open-Source ‘RAD’ To Improve Any Reinforcement Learning Algorithm

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In Reinforcement Learning (RL), it has always been challenging to learn from visual observations, which is a fundamental yet challenging problem. Despite algorithmic advancements combined with convolutional neural networks, current methods for learning from visual observations still lack on two fronts: (a) sample efficiency of learning, and (b) generalization to new environments.

To solve this problem of Reinforcement Learning (RL) to learn from visual observations, a group of University of California, Berkeley researchers have open-sourced Reinforcement Learning with Augmented Data (‘RAD’). In the latest release of ‘RAD’, published in arXiv, the research team explains how this Augmented Data based simple plug-and-play module (‘RAD’) can enhance any Reinforcement Learning (RL) algorithm. 

Data augmentation techniques increase diversity in training data sets without collecting a new set of data. ‘RAD’ achieves state-of-the-art in terms of data-efficiency and performance across 15 environments based on the DeepMind Control Suite. According to the research team, ‘RAD’ can improve any existing reinforcement learning algorithm, and it achieves better compute and data efficiency than Google AI’s PlaNet.

Release platform: https://mishalaskin.github.io/rad/

Github: https://github.com/MishaLaskin/rad

Paper: https://arxiv.org/abs/2004.14990

https://arxiv.org/pdf/2004.14990.pdf

Asif Razzaqhttp://www.marktechpost.com
Asif Razzaq is an AI Journalist and Cofounder of Marktechpost, LLC. He is a visionary, entrepreneur and engineer who aspires to use the power of Artificial Intelligence for good. Asif's latest venture is the development of an Artificial Intelligence Media Platform (Marktechpost) that will revolutionize how people can find relevant news related to Artificial Intelligence, Data Science and Machine Learning. Asif was featured by Onalytica in it’s ‘Who’s Who in AI? (Influential Voices & Brands)’ as one of the 'Influential Journalists in AI' (https://onalytica.com/wp-content/uploads/2021/09/Whos-Who-In-AI.pdf). His interview was also featured by Onalytica (https://onalytica.com/blog/posts/interview-with-asif-razzaq/).

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