Matteo Hessel | DeepAI

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∙ 10/25/2021

Self-Consistent Models and Values

Learned models of the environment provide reinforcement learning (RL) ag...

6 Gregory Farquhar, et al. ∙

∙ 06/21/2021

Emphatic Algorithms for Deep Reinforcement Learning

Off-policy learning allows us to learn about possible policies of behavi...

0 Ray Jiang, et al. ∙

∙ 04/13/2021

Podracer architectures for scalable Reinforcement Learning

Supporting state-of-the-art AI research requires balancing rapid prototy...

0 Matteo Hessel, et al. ∙

∙ 04/13/2021

Muesli: Combining Improvements in Policy Optimization

We propose a novel policy update that combines regularized policy optimi...

0 Matteo Hessel, et al. ∙

∙ 02/12/2021

Discovery of Options via Meta-Learned Subgoals

Temporal abstractions in the form of options have been shown to help rei...

5 Vivek Veeriah, et al. ∙

∙ 07/17/2020

Discovering Reinforcement Learning Algorithms

Reinforcement learning (RL) algorithms update an agent's parameters acco...

72 Junhyuk Oh, et al. ∙

∙ 07/16/2020

Meta-Gradient Reinforcement Learning with an Objective Discovered Online

Deep reinforcement learning includes a broad family of algorithms that p...

9 Zhongwen Xu, et al. ∙

∙ 02/28/2020

Self-Tuning Deep Reinforcement Learning

Reinforcement learning (RL) algorithms often require expensive manual or...

20 Tom Zahavy, et al. ∙

∙ 12/11/2019

What Can Learned Intrinsic Rewards Capture?

Reinforcement learning agents can include different components, such as ...

25 Zeyu Zheng, et al. ∙

∙ 09/25/2019

Off-Policy Actor-Critic with Shared Experience Replay

We investigate the combination of actor-critic reinforcement learning al...

0 Simon Schmitt, et al. ∙

∙ 09/10/2019

Discovery of Useful Questions as Auxiliary Tasks

Arguably, intelligent agents ought to be able to discover their own ques...

7 Vivek Veeriah, et al. ∙

∙ 08/09/2019

Behaviour Suite for Reinforcement Learning

This paper introduces the Behaviour Suite for Reinforcement Learning, or...

2 Ian Osband, et al. ∙

∙ 07/08/2019

General non-linear Bellman equations

We consider a general class of non-linear Bellman equations. These open ...

5 Hado van Hasselt, et al. ∙

∙ 07/05/2019

On Inductive Biases in Deep Reinforcement Learning

Many deep reinforcement learning algorithms contain inductive biases tha...

6 Matteo Hessel, et al. ∙

∙ 06/12/2019

When to use parametric models in reinforcement learning?

We examine the question of when and how parametric models are most usefu...

0 Hado van Hasselt, et al. ∙

∙ 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...

12 Andre Barreto, et al. ∙

∙ 12/14/2018

Scaling shared model governance via model splitting

Currently the only techniques for sharing governance of a deep learning ...

6 Miljan Martic, et al. ∙

∙ 12/06/2018

Deep Reinforcement Learning and the Deadly Triad

We know from reinforcement learning theory that temporal difference lear...

0 Hado van Hasselt, et al. ∙

∙ 09/12/2018

Multi-task Deep Reinforcement Learning with PopArt

The reinforcement learning community has made great strides in designing...

0 Matteo Hessel, et al. ∙

∙ 05/29/2018

Observe and Look Further: Achieving Consistent Performance on Atari

Despite significant advances in the field of deep Reinforcement Learning...

0 Tobias Pohlen, et al. ∙

∙ 03/02/2018

Distributed Prioritized Experience Replay

We propose a distributed architecture for deep reinforcement learning at...

0 Dan Horgan, et al. ∙

∙ 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...

0 Daniel J. Mankowitz, et al. ∙

∙ 10/06/2017

Rainbow: Combining Improvements in Deep Reinforcement Learning

The deep reinforcement learning community has made several independent i...

0 Matteo Hessel, et al. ∙

∙ 12/28/2016

The Predictron: End-To-End Learning and Planning

One of the key challenges of artificial intelligence is to learn models ...

0 David Silver, et al. ∙

∙ 02/24/2016

Learning values across many orders of magnitude

Most learning algorithms are not invariant to the scale of the function ...

0 Hado van Hasselt, et al. ∙

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