
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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What Can Learned Intrinsic Rewards Capture?
Reinforcement learning agents can include different components, such as ...
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Hindsight Credit Assignment
We consider the problem of efficient credit assignment in reinforcement ...
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Conditional Importance Sampling for OffPolicy Learning
The principal contribution of this paper is a conceptual framework for o...
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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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Metalearning of Sequential Strategies
In this report we review memorybased metalearning as a tool for buildi...
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Universal Successor Features Approximators
The ability of a reinforcement learning (RL) agent to learn about many r...
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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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The Barbados 2018 List of Open Issues in Continual Learning
We want to make progress toward artificial general intelligence, namely ...
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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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MetaGradient Reinforcement Learning
The goal of reinforcement learning algorithms is to estimate and/or opti...
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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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StarCraft II: A New Challenge for Reinforcement Learning
This paper introduces SC2LE (StarCraft II Learning Environment), a reinf...
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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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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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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...
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Hado van Hasselt
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