Packing robot
Reinforcement Learning

Packing Robots Get a Grip

Robots are moving into a job that traditionally required the human touch.What’s new: A commercial warehouse that ships electrical supplies deployed AI-driven robotic arms from Covariant, a high-profile Silicon Valley robotics firm.
Maze action video game Pac-Man
Reinforcement Learning

Two-Way Winner

AlphaGo Zero demonstrates superhuman performance playing Go, chess, and shogi. Models like R2D2 do the same playing classic Atari titles. A new approach to deep reinforcement learning is the first to achieve state-of-the-art results playing both board and video games.
Yann LeCun
Reinforcement Learning

Yann LeCun: Learning From Observation

How is it that many people learn to drive a car fairly safely in 20 hours of practice, while current imitation learning algorithms take hundreds of thousands of hours, and reinforcement learning algorithms take millions of hours? Clearly we’re missing something big.
Chelsea Finn
Reinforcement Learning

Chelsea Finn: Robots That Generalize

Many people in the AI community focus on achieving flashy results, like building an agent that can win at Go or Jeopardy. This kind of work is impressive in terms of complexity.
Anima Anandkumar
Reinforcement Learning

Anima Anandkumar: The Power of Simulation

We’ve had great success with supervised deep learning on labeled data. Now it’s time to explore other ways to learn: training on unlabeled data, lifelong learning, and especially letting models explore a simulated environment before transferring what they learn to the real world.
Illustration of a crystal snowball
Reinforcement Learning

Simulation Substitutes for Data

The future of machine learning may depend less on amassing ground-truth data than simulating the environment in which a model will operate. Deep learning works like magic with enough high-quality data. When examples are scarce, though, researchers are using simulation to fill the gap.
Information related to Implicit Reinforcement without Interaction at Scale (IRIS)
Reinforcement Learning

Different Skills From Different Demos

Reinforcement learning trains models by trial and error. In batch reinforcement learning (BRL), models learn by observing many demonstrations by a variety of actors. But what if one doctor is handier with a scalpel while another excels at suturing?
Observational dropout
Reinforcement Learning

Seeing the World Blindfolded

In reinforcement learning, if researchers want an agent to have an internal representation of its environment, they’ll build and train a world model that it can refer to. New research shows that world models can emerge from standard training, rather than needing to be built separately.
AlphaGo playing Go with Lee Sedol
Reinforcement Learning

Is AI Making Mastery Obsolete?

Is there any reason to continue playing games that AI has mastered? Ask the former champions who have been toppled by machines.
Comparison between TrXL and GTrXL
Reinforcement Learning

Melding Transformers with RL

Large NLP models like BERT can answer questions about a document thanks to the transformer network, a sequence-processing architecture that retains information across much longer sequences than previous methods. But transformers have had little success in reinforcement learning — until now.
Bipedal robot crossing obstacles
Reinforcement Learning

Survival of the Overfittest

Neuroevolution, which combines neural networks with ideas drawn from Darwin, is gaining momentum. Its advocates claim that they can achieve faster, better results by generating a succession of new models, each slightly different than its predecessors, rather than relying on a purpose-built model.
StarCraft II videogame
Reinforcement Learning

Take That, Humans!

At the BlizzCon gaming convention last weekend, players of the strategy game StarCraft II stood in line to get walloped by DeepMind’s AI. After training for the better part of a year, the bot has become one of the world’s top players.
Mechanical hand unscrambling a Rubik's Cube
Reinforcement Learning

Cube Controversy

OpenAI trained a five-fingered robotic hand to unscramble the Rubik’s Cube puzzle, bringing both acclaim and criticism. The AI research lab OpenAI trained a mechanical hand to balance, twist, and turn the cube.
Schematic of the architecture used in experiments related to systematic reasoning in deep reinforcement learning
Reinforcement Learning

How Neural Networks Generalize

Humans understand the world by abstraction: If you grasp the concept of grabbing a stick, then you’ll also comprehend grabbing a ball. New work explores deep learning agents’ ability to do the same thing — an important aspect of their ability to generalize.
Robot being trained using a virtual reality interface
Reinforcement Learning

A Robot in Every Kitchen

Every home is different. That makes it difficult for domestic robots to translate skills learned in one household into another. Training in virtual reality, where the robot has access to rich information about three-dimensional objects, can make it easier to generalize skills to the real world.

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