Tag: Reinforcement Learning

AI Asia Technology

Preferred Networks Releases PFRL Deep Reinforcement Learning Library for PyTorch Users

PFRL succeeds ChainerRL as comprehensive library with cutting-edge deep reinforcement learning algorithms and features.

AI Technology United States

After Mastering Go and StarCraft, DeepMind Takes on Soccer

Having notched impressive victories over human professionals in Go, Atari Games, and most recently StarCraft 2 — Google’s DeepMind team has now turned its formidable research efforts to soccer. In a paper released last week, the UK AI company demonstrates a novel machine learning method that trains a team of AI agents to play a simulated version of “the beautiful game.”

AI Technology

Yann LeCun Cake Analogy 2.0

Facebook AI Chief Yann LeCun introduced his now-famous “cake analogy” at NIPS 2016: “If intelligence is a cake, the bulk of the cake is unsupervised learning, the icing on the cake is supervised learning, and the cherry on the cake is reinforcement learning (RL).”


You Can’t Keep an RL-Powered ANYmal Down

ANYmal does not have an easy life. One of the four-legged robot’s main tasks is to learn how to stand up again — no matter how many times it is kicked, pushed or otherwise tumbles to the ground. A research team from Switzerland’s ETH Zurich University trained ANYmal using reinforcement learning (RL) and published their work last Wednesday.

AI Technology

Get a Grip! Berkeley Targets Dexterous Manipulation Using Deep RL

UC Berkeley researchers have published a paper demonstrating how Deep Reinforcement Learning can be used to control dexterous robot hands for complicated tasks. Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations proposes a low-cost and high-efficiency control method that uses demonstration and simulation techniques to accelerate the learning process.

AI Technology

Harvard & University of Toronto Researchers Apply Deep Generative Models to Inverse Molecular Design

Benjamin Sanchez-Lengeling from Harvard University and Alán Aspuru-Guzik from the University of Toronto have successfully applied machine learning models to speed up the materials discovery process. Their paper Inverse molecular design using machine learning: Generative models for matter engineering was published July 27 in Science Vol. 361.