
Bayesian Optimization in AlphaGo
During the development of AlphaGo, its many hyperparameters were tuned ...
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What Can Learned Intrinsic Rewards Capture?
Reinforcement learning agents can include different components, such as ...
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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 ...
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SelfTuning Deep Reinforcement Learning
Reinforcement learning (RL) algorithms often require expensive manual or...
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Valuedriven Hindsight Modelling
Value estimation is a critical component of the reinforcement learning (...
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Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement
The ability to transfer skills across tasks has the potential to scale u...
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An investigation of modelfree planning
The field of reinforcement learning (RL) is facing increasingly challeng...
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Discovery of Useful Questions as Auxiliary Tasks
Arguably, intelligent agents ought to be able to discover their own ques...
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Universal Successor Features Approximators
The ability of a reinforcement learning (RL) agent to learn about many r...
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On Inductive Biases in Deep Reinforcement Learning
Many deep reinforcement learning algorithms contain inductive biases tha...
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Humanlevel performance in firstperson multiplayer games with populationbased deep reinforcement learning
Recent progress in artificial intelligence through reinforcement learnin...
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Behaviour Suite for Reinforcement Learning
This paper introduces the Behaviour Suite for Reinforcement Learning, or...
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Playing Atari with Deep Reinforcement Learning
We present the first deep learning model to successfully learn control p...
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Asynchronous Methods for Deep Reinforcement Learning
We propose a conceptually simple and lightweight framework for deep rein...
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Decoupled Neural Interfaces using Synthetic Gradients
Training directed neural networks typically requires forwardpropagating...
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Rainbow: Combining Improvements in Deep Reinforcement Learning
The deep reinforcement learning community has made several independent i...
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StarCraft II: A New Challenge for Reinforcement Learning
This paper introduces SC2LE (StarCraft II Learning Environment), a reinf...
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Reinforcement Learning with Unsupervised Auxiliary Tasks
Deep reinforcement learning agents have achieved stateoftheart result...
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Emergence of Locomotion Behaviours in Rich Environments
The reinforcement learning paradigm allows, in principle, for complex be...
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ImaginationAugmented Agents for Deep Reinforcement Learning
We introduce ImaginationAugmented Agents (I2As), a novel architecture f...
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Learning Continuous Control Policies by Stochastic Value Gradients
We present a unified framework for learning continuous control policies ...
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Massively Parallel Methods for Deep Reinforcement Learning
We present the first massively distributed architecture for deep reinfor...
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Move Evaluation in Go Using Deep Convolutional Neural Networks
The game of Go is more challenging than other board games, due to the di...
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The Predictron: EndToEnd Learning and Planning
One of the key challenges of artificial intelligence is to learn models ...
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Learning and Transfer of Modulated Locomotor Controllers
We study a novel architecture and training procedure for locomotion task...
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Successor Features for Transfer in Reinforcement Learning
Transfer in reinforcement learning refers to the notion that generalizat...
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Deep Reinforcement Learning from SelfPlay in ImperfectInformation Games
Many realworld applications can be described as largescale games of im...
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Learning values across many orders of magnitude
Most learning algorithms are not invariant to the scale of the function ...
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Learning to Win by Reading Manuals in a MonteCarlo Framework
Domain knowledge is crucial for effective performance in autonomous cont...
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Continuous control with deep reinforcement learning
We adapt the ideas underlying the success of Deep QLearning to the cont...
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Value Iteration with Options and State Aggregation
This paper presents a way of solving Markov Decision Processes that comb...
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Better Optimism By Bayes: Adaptive Planning with Rich Models
The computational costs of inference and planning have confined Bayesian...
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A Monte Carlo AIXI Approximation
This paper introduces a principled approach for the design of a scalable...
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Efficient BayesAdaptive Reinforcement Learning using SampleBased Search
Bayesian modelbased reinforcement learning is a formally elegant approa...
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Mastering Chess and Shogi by SelfPlay with a General Reinforcement Learning Algorithm
The game of chess is the most widelystudied domain in the history of ar...
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Unicorn: Continual Learning with a Universal, Offpolicy Agent
Some realworld domains are best characterized as a single task, but for...
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Learning to Search with MCTSnets
Planning problems are among the most important and wellstudied problems...
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Distributed Prioritized Experience Replay
We propose a distributed architecture for deep reinforcement learning at...
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Unsupervised Predictive Memory in a GoalDirected Agent
Animals execute goaldirected behaviours despite the limited range and s...
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MetaGradient Reinforcement Learning
The goal of reinforcement learning algorithms is to estimate and/or opti...
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Implicit Quantile Networks for Distributional Reinforcement Learning
In this work, we build on recent advances in distributional reinforcemen...
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A Unified GameTheoretic Approach to Multiagent Reinforcement Learning
To achieve general intelligence, agents must learn how to interact with ...
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Credit Assignment Techniques in Stochastic Computation Graphs
Stochastic computation graphs (SCGs) provide a formalism to represent st...
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