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Tag: reinforcement learning

Preserving Outputs Precisely while Adaptively Rescaling Targets

Multi-task learning - allowing a single agent to learn how to solve many different tasks - is a longstanding objective for artificial intelligence...

It’s 2018 – so, of course, VMware is touting open-source blockchain code, Internet-of-Things tools

VMworld US It's day two of VMware's VMworld 2018 US conference in Las Vegas, and here's a quick guide to what's new. The...

DeepMind papers at ICML 2018

Efficient Neural Audio SynthesisAuthors: Nal Kalchbrenner, Erich Elsen, Karen Simonyan, Seb Nouri, Norman Casagrande, Edward Lockhart, Sander Dieleman, Aaron van den Oord, Koray...

Prefrontal cortex as a meta-reinforcement learning system

In fact, we found that the meta-RL agent could learn to quickly adapt in a wide domain of tasks with different rules and...

DeepMind papers at ICLR 2018

Maximum a posteriori policy optimisationAuthors: Abbas Abdolmaleki, Jost Tobias Springenberg, Nicolas Heess, Yuval Tassa, Remi MunosWe introduce a new algorithm for reinforcement learning...

Learning to navigate in cities without a map

Learning navigation without building maps We depart from the traditional approaches which rely on explicit mapping and exploration (like a cartographer who tries to...

Learning by playing

Getting children (and adults) to tidy up after themselves can be a challenge, but we face an even greater challenge trying to get...

DeepMind papers at NIPS 2017

A simple neural network module for relational reasoning Authors: Adam Santoro, David Raposo, David Barrett, Mateusz Malinowski, Razvan Pascanu, Peter Battaglia, Timothy Lillicrap “We demonstrate...

The hippocampus as a predictive map

This approach combines the strengths of two algorithms that are already well known in reinforcement learning and are also believed to exist in...

DeepMind papers at ICML 2017 (part two)

DARLA: Improving Zero-Shot Transfer in Reinforcement LearningAuthors: Irina Higgins*, Arka Pal*, Andrei Rusu, Loic Matthey, Chris Burgess, Alexander Pritzel, Matt Botvinick, Charles Blundell,...

DeepMind papers at ICML 2017 (part three)

Neural Episodic ControlAuthors: Alex Pritzel, Benigno Uria, Sriram Srinivasan, Adria Puigdomenech, Oriol Vinyals, Demis Hassabis, Daan Wierstra, Charles BlundellDeep reinforcement learning algorithms have...

AI and Neuroscience: A virtuous circle

Another key challenge in contemporary AI research is known as transfer learning. To be able to deal effectively with novel situations, artificial agents...

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