Hado van Hasselt

research

∙ 03/07/2023

Exploration via Epistemic Value Estimation

How to efficiently explore in reinforcement learning is an open problem....

0 Simon Schmitt, et al. ∙

research

∙ 02/08/2023

Learning How to Infer Partial MDPs for In-Context Adaptation and Exploration

To generalize across tasks, an agent should acquire knowledge from past ...

0 Chentian Jiang, et al. ∙

research

∙ 09/15/2022

Human-level Atari 200x faster

The task of building general agents that perform well over a wide range ...

0 Steven Kapturowski, et al. ∙

research

∙ 02/20/2022

Selective Credit Assignment

Efficient credit assignment is essential for reinforcement learning algo...

0 Veronica Chelu, et al. ∙

research

∙ 01/17/2022

Chaining Value Functions for Off-Policy Learning

To accumulate knowledge and improve its policy of behaviour, a reinforce...

2 Simon Schmitt, et al. ∙

research

∙ 10/25/2021

Self-Consistent Models and Values

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

6 Gregory Farquhar, et al. ∙

research

∙ 10/08/2021

Pick Your Battles: Interaction Graphs as Population-Level Objectives for Strategic Diversity

Strategic diversity is often essential in games: in multi-player games, ...

0 Marta Garnelo, et al. ∙

research

∙ 09/09/2021

Bootstrapped Meta-Learning

Meta-learning empowers artificial intelligence to increase its efficienc...

23 Sebastian Flennerhag, et al. ∙

research

∙ 07/12/2021

Learning Expected Emphatic Traces for Deep RL

Off-policy sampling and experience replay are key for improving sample e...

0 Ray Jiang, et al. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 02/24/2021

Synthetic Returns for Long-Term Credit Assignment

Since the earliest days of reinforcement learning, the workhorse method ...

6 David Raposo, et al. ∙

research

∙ 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. ∙

research

∙ 10/26/2020

Forethought and Hindsight in Credit Assignment

We address the problem of credit assignment in reinforcement learning an...

0 Veronica Chelu, et al. ∙

research

∙ 07/17/2020

Discovering Reinforcement Learning Algorithms

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

72 Junhyuk Oh, et al. ∙

research

∙ 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. ∙

research

∙ 12/11/2019

What Can Learned Intrinsic Rewards Capture?

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

25 Zeyu Zheng, et al. ∙

research

∙ 12/05/2019

Hindsight Credit Assignment

We consider the problem of efficient credit assignment in reinforcement ...

0 Anna Harutyunyan, et al. ∙

research

∙ 10/16/2019

Conditional Importance Sampling for Off-Policy Learning

The principal contribution of this paper is a conceptual framework for o...

12 Mark Rowland, et al. ∙

research

∙ 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. ∙

research

∙ 08/09/2019

Behaviour Suite for Reinforcement Learning

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

2 Ian Osband, et al. ∙

research

∙ 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. ∙

research

∙ 07/05/2019

On Inductive Biases in Deep Reinforcement Learning

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

6 Matteo Hessel, et al. ∙

research

∙ 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. ∙

research

∙ 05/08/2019

Meta-learning of Sequential Strategies

In this report we review memory-based meta-learning as a tool for buildi...

16 Pedro A. Ortega, et al. ∙

research

∙ 12/18/2018

Universal Successor Features Approximators

The ability of a reinforcement learning (RL) agent to learn about many r...

6 Diana Borsa, et al. ∙

research

∙ 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. ∙

research

∙ 11/16/2018

The Barbados 2018 List of Open Issues in Continual Learning

We want to make progress toward artificial general intelligence, namely ...

0 Tom Schaul, et al. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 05/24/2018

Meta-Gradient Reinforcement Learning

The goal of reinforcement learning algorithms is to estimate and/or opti...

0 Zhongwen Xu, et al. ∙

research

∙ 03/02/2018

Distributed Prioritized Experience Replay

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

0 Dan Horgan, et al. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 08/16/2017

StarCraft II: A New Challenge for Reinforcement Learning

This paper introduces SC2LE (StarCraft II Learning Environment), a reinf...

0 Oriol Vinyals, et al. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 12/24/2015

Deep Reinforcement Learning in Large Discrete Action Spaces

Being able to reason in an environment with a large number of discrete a...

0 Gabriel Dulac-Arnold, et al. ∙

research

∙ 02/28/2013

Estimating the Maximum Expected Value: An Analysis of (Nested) Cross Validation and the Maximum Sample Average

We investigate the accuracy of the two most common estimators for the ma...

0 Hado van Hasselt, et al. ∙

Hado van Hasselt

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