1.8k citations · 4.8k across the 37 of their papers we have counts for
8 papers · 1 filter
Universal Successor Features Approximators
Diana Borsa, André Barreto, John Quan +5
The ability of a reinforcement learning (RL) agent to learn about many reward functions at the same time has many potential benefits, such as the decomposition of complex tasks int…
Deep Reinforcement Learning and the Deadly Triad
Hado van Hasselt, Yotam Doron, Florian Strub +3
We know from reinforcement learning theory that temporal difference learning can fail in certain cases. Sutton and Barto (2018) identify a deadly triad of function approximation, b…
The Barbados 2018 List of Open Issues in Continual Learning
Tom Schaul, Hado van Hasselt, Joseph Modayil +7
We want to make progress toward artificial general intelligence, namely general-purpose agents that autonomously learn how to competently act in complex environments. The purpose o…
Multi-task Deep Reinforcement Learning with PopArt
Matteo Hessel, Hubert Soyer, Lasse Espeholt +3
The reinforcement learning community has made great strides in designing algorithms capable of exceeding human performance on specific tasks. These algorithms are mostly trained on…
Observe and Look Further: Achieving Consistent Performance on Atari
Tobias Pohlen, Bilal Piot, Todd Hester +10
Despite significant advances in the field of deep Reinforcement Learning (RL), today's algorithms still fail to learn human-level policies consistently over a set of diverse tasks…
Meta-Gradient Reinforcement Learning
Zhongwen Xu, Hado van Hasselt, David Silver
The goal of reinforcement learning algorithms is to estimate and/or optimise the value function. However, unlike supervised learning, no teacher or oracle is available to provide t…
Distributed Prioritized Experience Replay
Dan Horgan, John Quan, David Budden +4
We propose a distributed architecture for deep reinforcement learning at scale, that enables agents to learn effectively from orders of magnitude more data than previously possible…
Unicorn: Continual Learning with a Universal, Off-policy Agent
Daniel J. Mankowitz, Augustin Žídek, André Barreto +7
Some real-world domains are best characterized as a single task, but for others this perspective is limiting. Instead, some tasks continually grow in complexity, in tandem with the…