DeepMind Deep Reinforcement Learning course 2018

04 - Model-Free Prediction and Control

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DeepMind Deep Reinforcement Learning course 2018

05 - Function Approximation and Deep Reinforcement Learning

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DeepMind Deep Reinforcement Learning course 2018

06 - Policy Gradients and Actor Critics

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DeepMind Deep Reinforcement Learning course 2018

07 - Planning and Models

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DeepMind Deep Reinforcement Learning course 2018

08 - Advanced Topics in Deep RL

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DeepMind Deep Reinforcement Learning course 2018

09 - A Brief Tour of Deep RL Agents

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DeepMind Deep Reinforcement Learning course 2018

10 - Classic Games Case Study

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DeepMind Deep Reinforcement Learning course 2018 (all lectures and slides) - part1

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DeepMind Deep Reinforcement Learning course 2018 (all lectures and slides) - part2

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DeepMind Deep Reinforcement Learning course 2018 (all lectures and slides) - part3

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Grandmaster level in #StarCraft II using multi-agent #ReinforcementLearning

A new paper by #DeepMind is going to be on the cover of #Nature this week:

Abstract:
Many real-world applications require artificial agents to compete and coordinate with other agents in complex environments. As a stepping stone to this goal, the domain of StarCraft has emerged as an important challenge for artificial intelligence research, owing to its iconic and enduring status among the most difficult professional esports and its relevance to the real world in terms of its raw complexity and multi-agent challenges. Over the course of a decade and numerous competitions, the strongest agents have simplified important aspects of the game, utilized superhuman capabilities, or employed hand-crafted sub-systems. Despite these advantages, no previous agent has come close to matching the overall skill of top StarCraft players. We chose to address the challenge of StarCraft using generalpurpose learning methods that are in principle applicable to other complex domains: a multi-agent reinforcement learning algorithm that uses data from both human and agent games within a diverse league of continually adapting strategies and counter-strategies, each represented by deep neural networks. We evaluated our agent, AlphaStar, in the full game of StarCraft II, through a series of online games against human players. AlphaStar was rated at Grandmaster level for all three StarCraft races and above 99.8% of officially ranked human players.

Link to the paper

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Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Constructing agents with planning capabilities has long been one of the main challenges in the pursuit of artificial intelligence. Tree-based planning methods have enjoyed huge success in challenging domains, such as chess and Go, where a perfect simulator is available. However, in real-world problems the dynamics governing the environment are often complex and unknown. In this work we present the MuZero algorithm which, by combining a tree-based search with a learned model, achieves superhuman performance in a range of challenging and visually complex domains, without any knowledge of their underlying dynamics. MuZero learns a model that, when applied iteratively, predicts the quantities most directly relevant to planning: the reward, the action-selection policy, and the value function. When evaluated on 57 different Atari games - the canonical video game environment for testing AI techniques, in which model-based planning approaches have historically struggled - our new algorithm achieved a new state of the art. When evaluated on Go, chess and shogi, without any knowledge of the game rules, MuZero matched the superhuman performance of the AlphaZero algorithm that was supplied with the game rules.

Link to the main paper

Link to a related article

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#DeepMind
#ReinforcementLearning

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#Google #DeepMind gamifies memory with its latest #AI work

Google DeepMind scientists built a computer program that gives signals from future to past, in a kind of theoretical model that feels like things people do when they learn from their mistakes. Just remember, it's only a game.

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Meta-gradient updates for training return functions for #ReinforcementLearning systems,

Abstract
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for reinforcement learning. The embodiments described herein apply meta-learning (and in particular, meta-gradient reinforcement learning) to learn an optimum return function G so that the training of the system is improved. This provides a more effective and efficient means of training a reinforcement learning system as the system is able to converge on an optimum set of one or more policy parameters θ more quickly by training the return function G as it goes. In particular, the return function G is made dependent on the one or more policy parameters θ and a meta-objective function J′ is used that is differentiated with respect to the one or more return parameters η to improve the training of the return function G.

#Google
#DeepMind

Paper

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#DeepMind ’s Dreamer #AI learns from the past to predict the future

Some AI systems achieve goals in challenging environments by drawing on representations of the world informed by past experiences. They generalize these to novel situations, enabling them to complete tasks even in settings they haven’t encountered before. As it turns out, reinforcement learning — a training technique that employs rewards to drive software policies toward goals — is particularly well-suited to learning world models that summarize an agent’s experience, and by extension to facilitating the learning of novel behaviors.

Article

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Learning human objectives by evaluating hypothetical behaviours

TL;DR: We present a method for training #ReinforcementLearning agents from human feedback in the presence of unknown unsafe states.

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Inside #DeepMind 's epic mission to solve science's trickiest problem
DeepMind's AI has beaten chess grandmasters and #Go champions. But founder and CEO Demis Hassabis now has his sights set on bigger, real-world problems that could change lives

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Using #AI to improve breast #cancer screening

Article

Paper

#Goolge
#DeepMind
#Nature

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#DeepMind researchers introduce hybrid solution to #robot #control problems

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#DeepMind AI Reduces #Google Data Centre Cooling Bill by 40%

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