Published in


UC Berkeley Reward-Free RL Beats SOTA Reward-Based RL

End-to-end Deep Reinforcement Learning (DRL) is a trending training approach in the field of computer vision, where it has proven successful at solving a wide range of complex tasks that were previously regarded as out of reach. End-to-end DRL is now being applied in domains ranging from real-world and simulated robotics to sophisticated video games. However, as appealing as end-to-end DRL methods…




We produce professional, authoritative, and thought-provoking content relating to artificial intelligence, machine intelligence, emerging technologies and industrial insights.

Recommended from Medium

Deep fakes are on the rise. What can you do?

a perfectly made up woman with fake eyelashes

NLG in conversational AI: The challenges of generating language

An animated image of a robot talking to depict conversational AI.

Epic-Kitchens | Largest Egocentric Video Dataset Gets New Baselines

Former Microsoft AI Head Harry Shum Joins Intelligent Local News Startup News Break as Chairman

Putting CFOs in the Driver’s Seat With Hyperautomation

The Future of AI-Fueled Customer Service Is Now

Wag the Tail: Getting the Most Out of Your Product Catalog

The job-Terminator invasion

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store


AI Technology & Industry Review — | Newsletter: | Share My Research | Twitter: @Synced_Global

More from Medium

Toward Large-Scale Edge AI Adoption:

Inside Meta’s New Architecture for Build AI Agents that Can Reason Like Humans and Animals

DeepMind Trains AI Agents Capable of Robust Real-time Cultural Transmission Without Human Data

Uber Work on Differential Plasticity in Deep Learning