Publications (44)
StarCraft II: A New Challenge for Reinforcement Learning
Oriol Vinyals, Timo Ewalds, Sergey Bartunov +22
Value Iteration with Options and State Aggregation
Kamil Ciosek, David Silver
Human-level performance in first-person multiplayer games with population-based deep reinforcement learning
Max Jaderberg, Wojciech M. Czarnecki, Iain Dunning +15
Reinforcement Learning via AIXI Approximation
Joel Veness, Kee Siong Ng, Marcus Hutter +1
Reinforcement Learning with Unsupervised Auxiliary Tasks
Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki +4
Continuous control with deep reinforcement learning
Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel +5
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
David Silver, Thomas Hubert, Julian Schrittwieser +10
Imagination-Augmented Agents for Deep Reinforcement Learning
Théophane Weber, Sébastien Racanière, David P. Reichert +12
Unicorn: Continual Learning with a Universal, Off-policy Agent
Daniel J. Mankowitz, Augustin ŽÃdek, André Barreto +7
FeUdal Networks for Hierarchical Reinforcement Learning
Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul +4
Deep Reinforcement Learning with Double Q-learning
Hado van Hasselt, Arthur Guez, David Silver
Deep Reinforcement Learning from Self-Play in Imperfect-Information Games
Johannes Heinrich, David Silver
Massively Parallel Methods for Deep Reinforcement Learning
Arun Nair, Praveen Srinivasan, Sam Blackwell +11
Decoupled Neural Interfaces using Synthetic Gradients
Max Jaderberg, Wojciech Marian Czarnecki, Simon Osindero +4
Better Optimism By Bayes: Adaptive Planning with Rich Models
Arthur Guez, David Silver, Peter Dayan
Learning to Win by Reading Manuals in a Monte-Carlo Framework
S. R. K. Branavan, David Silver, Regina Barzilay
Unit Tests for Stochastic Optimization
Tom Schaul, Ioannis Antonoglou, David Silver
Meta-Gradient Reinforcement Learning
Zhongwen Xu, Hado van Hasselt, David Silver
Rainbow: Combining Improvements in Deep Reinforcement Learning
Matteo Hessel, Joseph Modayil, Hado van Hasselt +7
A Monte Carlo AIXI Approximation
Joel Veness, Kee Siong Ng, Marcus Hutter +2
Unsupervised Predictive Memory in a Goal-Directed Agent
Greg Wayne, Chia-Chun Hung, David Amos +21
A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning
Marc Lanctot, Vinicius Zambaldi, Audrunas Gruslys +5
The Predictron: End-To-End Learning and Planning
David Silver, Hado van Hasselt, Matteo Hessel +8
Learning Continuous Control Policies by Stochastic Value Gradients
Nicolas Heess, Greg Wayne, David Silver +3
Emergence of Locomotion Behaviours in Rich Environments
Nicolas Heess, Dhruva TB, Srinivasan Sriram +9
Prioritized Experience Replay
Tom Schaul, John Quan, Ioannis Antonoglou +1
Successor Features for Transfer in Reinforcement Learning
André Barreto, Will Dabney, Rémi Munos +4
Memory-based control with recurrent neural networks
Nicolas Heess, Jonathan J Hunt, Timothy P Lillicrap +1
An investigation of model-free planning
Arthur Guez, Mehdi Mirza, Karol Gregor +10
Learning and Transfer of Modulated Locomotor Controllers
Nicolas Heess, Greg Wayne, Yuval Tassa +3
Learning to Search with MCTSnets
Arthur Guez, Théophane Weber, Ioannis Antonoglou +5
Distributed Prioritized Experience Replay
Dan Horgan, John Quan, David Budden +4
Implicit Quantile Networks for Distributional Reinforcement Learning
Will Dabney, Georg Ostrovski, David Silver +1
Learning values across many orders of magnitude
Hado van Hasselt, Arthur Guez, Matteo Hessel +2
Asynchronous Methods for Deep Reinforcement Learning
Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza +5
Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search
Arthur Guez, David Silver, Peter Dayan
Playing Atari with Deep Reinforcement Learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver +4
Bayesian Optimization in AlphaGo
Yutian Chen, Aja Huang, Ziyu Wang +4
Universal Successor Features Approximators
Diana Borsa, André Barreto, John Quan +5
Compositional Planning Using Optimal Option Models
David Silver, Kamil Ciosek
Credit Assignment Techniques in Stochastic Computation Graphs
Théophane Weber, Nicolas Heess, Lars Buesing +1
On Inductive Biases in Deep Reinforcement Learning
Matteo Hessel, Hado van Hasselt, Joseph Modayil +1
Move Evaluation in Go Using Deep Convolutional Neural Networks
Chris J. Maddison, Aja Huang, Ilya Sutskever +1
Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement
André Barreto, Diana Borsa, John Quan +6