
Statistics and Samples in Distributional Reinforcement Learning
We present a unifying framework for designing and analysing distribution...
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The Hanabi Challenge: A New Frontier for AI Research
From the early days of computing, games have been important testbeds for...
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An Introduction to Deep Reinforcement Learning
Deep reinforcement learning is the combination of reinforcement learning...
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Distributional reinforcement learning with linear function approximation
Despite many algorithmic advances, our theoretical understanding of prac...
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OffPolicy Deep Reinforcement Learning by Bootstrapping the Covariate Shift
In this paper we revisit the method of offpolicy corrections for reinfo...
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The Value Function Polytope in Reinforcement Learning
We establish geometric and topological properties of the space of value ...
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A Geometric Perspective on Optimal Representations for Reinforcement Learning
This paper proposes a new approach to representation learning based on g...
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Shaping the Narrative Arc: An InformationTheoretic Approach to Collaborative Dialogue
We consider the problem of designing an artificial agent capable of inte...
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A Comparative Analysis of Expected and Distributional Reinforcement Learning
Since their introduction a year ago, distributional approaches to reinfo...
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CountBased Exploration with the Successor Representation
The problem of exploration in reinforcement learning is wellunderstood ...
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Hyperbolic Discounting and Learning over Multiple Horizons
Reinforcement learning (RL) typically defines a discount factor as part ...
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DeepMDP: Learning Continuous Latent Space Models for Representation Learning
Many reinforcement learning (RL) tasks provide the agent with highdimen...
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Algorithmic Improvements for Deep Reinforcement Learning applied to Interactive Fiction
Textbased games are a natural challenge domain for deep reinforcement l...
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Approximate Exploration through State Abstraction
Although exploration in reinforcement learning is well understood from a...
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Benchmarking BonusBased Exploration Methods on the Arcade Learning Environment
This paper provides an empirical evaluation of recently developed explor...
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Dopamine: A Research Framework for Deep Reinforcement Learning
Deep reinforcement learning (deep RL) research has grown significantly i...
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Automated Curriculum Learning for Neural Networks
We introduce a method for automatically selecting the path, or syllabus,...
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Distributional Reinforcement Learning with Quantile Regression
In reinforcement learning an agent interacts with the environment by tak...
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A Distributional Perspective on Reinforcement Learning
In this paper we argue for the fundamental importance of the value distr...
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The Reactor: A SampleEfficient ActorCritic Architecture
In this work we present a new reinforcement learning agent, called React...
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The Cramer Distance as a Solution to Biased Wasserstein Gradients
The Wasserstein probability metric has received much attention from the ...
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Unifying CountBased Exploration and Intrinsic Motivation
We consider an agent's uncertainty about its environment and the problem...
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Increasing the Action Gap: New Operators for Reinforcement Learning
This paper introduces new optimalitypreserving operators on Qfunctions...
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Safe and Efficient OffPolicy Reinforcement Learning
In this work, we take a fresh look at some old and new algorithms for of...
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Compress and Control
This paper describes a new informationtheoretic policy evaluation techn...
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Q(λ) with OffPolicy Corrections
We propose and analyze an alternate approach to offpolicy multistep te...
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The Arcade Learning Environment: An Evaluation Platform for General Agents
In this article we introduce the Arcade Learning Environment (ALE): both...
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An Analysis of Categorical Distributional Reinforcement Learning
Distributional approaches to valuebased reinforcement learning model th...
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Marc G. Bellemare
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Research Scientist at Google Brain, Adjunct Professor at McGill University