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06/30/2022
Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning
We introduce DeepNash, an autonomous agent capable of learning to play t...
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10/25/2021
Self-Consistent Models and Values
Learned models of the environment provide reinforcement learning (RL) ag...
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09/09/2021
Bootstrapped Meta-Learning
Meta-learning empowers artificial intelligence to increase its efficienc...
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04/13/2021
Learning and Planning in Complex Action Spaces
Many important real-world problems have action spaces that are high-dime...
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04/13/2021
Online and Offline Reinforcement Learning by Planning with a Learned Model
Learning efficiently from small amounts of data has long been the focus ...
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04/13/2021
Muesli: Combining Improvements in Policy Optimization
We propose a novel policy update that combines regularized policy optimi...
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02/12/2021
Discovery of Options via Meta-Learned Subgoals
Temporal abstractions in the form of options have been shown to help rei...
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07/17/2020
Discovering Reinforcement Learning Algorithms
Reinforcement learning (RL) algorithms update an agent's parameters acco...
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07/16/2020
Meta-Gradient Reinforcement Learning with an Objective Discovered Online
Deep reinforcement learning includes a broad family of algorithms that p...
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02/28/2020
Self-Tuning Deep Reinforcement Learning
Reinforcement learning (RL) algorithms often require expensive manual or...
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02/19/2020
Value-driven Hindsight Modelling
Value estimation is a critical component of the reinforcement learning (...
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12/11/2019
What Can Learned Intrinsic Rewards Capture?
Reinforcement learning agents can include different components, such as ...
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11/19/2019
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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09/10/2019
Discovery of Useful Questions as Auxiliary Tasks
Arguably, intelligent agents ought to be able to discover their own ques...
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08/09/2019
Behaviour Suite for Reinforcement Learning
This paper introduces the Behaviour Suite for Reinforcement Learning, or...
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07/05/2019
On Inductive Biases in Deep Reinforcement Learning
Many deep reinforcement learning algorithms contain inductive biases tha...
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01/30/2019
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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01/11/2019
An investigation of model-free planning
The field of reinforcement learning (RL) is facing increasingly challeng...
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01/07/2019
Credit Assignment Techniques in Stochastic Computation Graphs
Stochastic computation graphs (SCGs) provide a formalism to represent st...
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12/18/2018
Universal Successor Features Approximators
The ability of a reinforcement learning (RL) agent to learn about many r...
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12/17/2018
Bayesian Optimization in AlphaGo
During the development of AlphaGo, its many hyper-parameters were tuned ...
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07/03/2018
Human-level performance in first-person multiplayer games with population-based deep reinforcement learning
Recent progress in artificial intelligence through reinforcement learnin...
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06/14/2018
Implicit Quantile Networks for Distributional Reinforcement Learning
In this work, we build on recent advances in distributional reinforcemen...
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05/24/2018
Meta-Gradient Reinforcement Learning
The goal of reinforcement learning algorithms is to estimate and/or opti...
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03/28/2018
Unsupervised Predictive Memory in a Goal-Directed Agent
Animals execute goal-directed behaviours despite the limited range and s...
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03/02/2018
Distributed Prioritized Experience Replay
We propose a distributed architecture for deep reinforcement learning at...
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02/22/2018
Unicorn: Continual Learning with a Universal, Off-policy Agent
Some real-world domains are best characterized as a single task, but for...
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02/13/2018
Learning to Search with MCTSnets
Planning problems are among the most important and well-studied problems...
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12/05/2017
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
The game of chess is the most widely-studied domain in the history of ar...
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11/02/2017
A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning
To achieve general intelligence, agents must learn how to interact with ...
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10/06/2017
Rainbow: Combining Improvements in Deep Reinforcement Learning
The deep reinforcement learning community has made several independent i...
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08/16/2017
StarCraft II: A New Challenge for Reinforcement Learning
This paper introduces SC2LE (StarCraft II Learning Environment), a reinf...
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07/19/2017
Imagination-Augmented Agents for Deep Reinforcement Learning
We introduce Imagination-Augmented Agents (I2As), a novel architecture f...
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07/07/2017
Emergence of Locomotion Behaviours in Rich Environments
The reinforcement learning paradigm allows, in principle, for complex be...
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12/28/2016
The Predictron: End-To-End Learning and Planning
One of the key challenges of artificial intelligence is to learn models ...
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11/16/2016
Reinforcement Learning with Unsupervised Auxiliary Tasks
Deep reinforcement learning agents have achieved state-of-the-art result...
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10/17/2016
Learning and Transfer of Modulated Locomotor Controllers
We study a novel architecture and training procedure for locomotion task...
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08/18/2016
Decoupled Neural Interfaces using Synthetic Gradients
Training directed neural networks typically requires forward-propagating...
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06/16/2016
Successor Features for Transfer in Reinforcement Learning
Transfer in reinforcement learning refers to the notion that generalizat...
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03/03/2016
Deep Reinforcement Learning from Self-Play in Imperfect-Information Games
Many real-world applications can be described as large-scale games of im...
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02/24/2016
Learning values across many orders of magnitude
Most learning algorithms are not invariant to the scale of the function ...
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02/04/2016
Asynchronous Methods for Deep Reinforcement Learning
We propose a conceptually simple and lightweight framework for deep rein...
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10/30/2015
Learning Continuous Control Policies by Stochastic Value Gradients
We present a unified framework for learning continuous control policies ...
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09/09/2015
Continuous control with deep reinforcement learning
We adapt the ideas underlying the success of Deep Q-Learning to the cont...
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07/15/2015
Massively Parallel Methods for Deep Reinforcement Learning
We present the first massively distributed architecture for deep reinfor...
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01/16/2015
Value Iteration with Options and State Aggregation
This paper presents a way of solving Markov Decision Processes that comb...
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12/20/2014
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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02/09/2014
Better Optimism By Bayes: Adaptive Planning with Rich Models
The computational costs of inference and planning have confined Bayesian...
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01/18/2014
Learning to Win by Reading Manuals in a Monte-Carlo Framework
Domain knowledge is crucial for effective performance in autonomous cont...
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12/19/2013
