
Discovering Reinforcement Learning Algorithms
Reinforcement learning (RL) algorithms update an agent's parameters acco...
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MetaGradient Reinforcement Learning with an Objective Discovered Online
Deep reinforcement learning includes a broad family of algorithms that p...
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SelfTuning Deep Reinforcement Learning
Reinforcement learning (RL) algorithms often require expensive manual or...
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What Can Learned Intrinsic Rewards Capture?
Reinforcement learning agents can include different components, such as ...
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OffPolicy ActorCritic with Shared Experience Replay
We investigate the combination of actorcritic reinforcement learning al...
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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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Behaviour Suite for Reinforcement Learning
This paper introduces the Behaviour Suite for Reinforcement Learning, or...
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General nonlinear Bellman equations
We consider a general class of nonlinear Bellman equations. These open ...
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On Inductive Biases in Deep Reinforcement Learning
Many deep reinforcement learning algorithms contain inductive biases tha...
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When to use parametric models in reinforcement learning?
We examine the question of when and how parametric models are most usefu...
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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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Scaling shared model governance via model splitting
Currently the only techniques for sharing governance of a deep learning ...
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Deep Reinforcement Learning and the Deadly Triad
We know from reinforcement learning theory that temporal difference lear...
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Multitask Deep Reinforcement Learning with PopArt
The reinforcement learning community has made great strides in designing...
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Observe and Look Further: Achieving Consistent Performance on Atari
Despite significant advances in the field of deep Reinforcement Learning...
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Distributed Prioritized Experience Replay
We propose a distributed architecture for deep reinforcement learning at...
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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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Rainbow: Combining Improvements in Deep Reinforcement Learning
The deep reinforcement learning community has made several independent i...
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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 values across many orders of magnitude
Most learning algorithms are not invariant to the scale of the function ...
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Matteo Hessel
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