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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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06/21/2021
Emphatic Algorithms for Deep Reinforcement Learning
Off-policy learning allows us to learn about possible policies of behavi...
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04/13/2021
Podracer architectures for scalable Reinforcement Learning
Supporting state-of-the-art AI research requires balancing rapid prototy...
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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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12/11/2019
What Can Learned Intrinsic Rewards Capture?
Reinforcement learning agents can include different components, such as ...
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09/25/2019
Off-Policy Actor-Critic with Shared Experience Replay
We investigate the combination of actor-critic reinforcement learning al...
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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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07/08/2019
General non-linear Bellman equations
We consider a general class of non-linear Bellman equations. These open ...
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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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06/12/2019
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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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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12/14/2018
Scaling shared model governance via model splitting
Currently the only techniques for sharing governance of a deep learning ...
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12/06/2018
Deep Reinforcement Learning and the Deadly Triad
We know from reinforcement learning theory that temporal difference lear...
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09/12/2018
Multi-task Deep Reinforcement Learning with PopArt
The reinforcement learning community has made great strides in designing...
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05/29/2018
Observe and Look Further: Achieving Consistent Performance on Atari
Despite significant advances in the field of deep Reinforcement Learning...
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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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10/06/2017
Rainbow: Combining Improvements in Deep Reinforcement Learning
The deep reinforcement learning community has made several independent i...
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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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02/24/2016