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03/07/2023
Exploration via Epistemic Value Estimation
How to efficiently explore in reinforcement learning is an open problem....
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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 ...
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09/15/2022
Human-level Atari 200x faster
The task of building general agents that perform well over a wide range ...
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02/20/2022
Selective Credit Assignment
Efficient credit assignment is essential for reinforcement learning algo...
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01/17/2022
Chaining Value Functions for Off-Policy Learning
To accumulate knowledge and improve its policy of behaviour, a reinforce...
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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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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, ...
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09/09/2021
Bootstrapped Meta-Learning
Meta-learning empowers artificial intelligence to increase its efficienc...
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07/12/2021
Learning Expected Emphatic Traces for Deep RL
Off-policy sampling and experience replay are key for improving sample e...
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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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02/24/2021
Synthetic Returns for Long-Term Credit Assignment
Since the earliest days of reinforcement learning, the workhorse method ...
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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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10/26/2020
Forethought and Hindsight in Credit Assignment
We address the problem of credit assignment in reinforcement learning an...
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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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12/11/2019
What Can Learned Intrinsic Rewards Capture?
Reinforcement learning agents can include different components, such as ...
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12/05/2019
Hindsight Credit Assignment
We consider the problem of efficient credit assignment in reinforcement ...
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10/16/2019
Conditional Importance Sampling for Off-Policy Learning
The principal contribution of this paper is a conceptual framework for o...
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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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05/08/2019
Meta-learning of Sequential Strategies
In this report we review memory-based meta-learning as a tool for buildi...
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12/18/2018
Universal Successor Features Approximators
The ability of a reinforcement learning (RL) agent to learn about many r...
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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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11/16/2018
The Barbados 2018 List of Open Issues in Continual Learning
We want to make progress toward artificial general intelligence, namely ...
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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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05/24/2018
Meta-Gradient Reinforcement Learning
The goal of reinforcement learning algorithms is to estimate and/or opti...
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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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08/16/2017
StarCraft II: A New Challenge for Reinforcement Learning
This paper introduces SC2LE (StarCraft II Learning Environment), a reinf...
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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
Learning values across many orders of magnitude
Most learning algorithms are not invariant to the scale of the function ...
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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...
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02/28/2013
