Marc G. Bellemare

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Google ∙ University of Alberta

Research Scientist at Google Brain, Adjunct Professor at McGill University

Statistics and Samples in Distributional Reinforcement Learning

We present a unifying framework for designing and analysing distribution...
02/21/2019 ∙ by Mark Rowland, et al. ∙ 64 ∙ share

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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...
02/01/2019 ∙ by Nolan Bard, et al. ∙ 54 ∙ share

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An Introduction to Deep Reinforcement Learning

Deep reinforcement learning is the combination of reinforcement learning...
11/30/2018 ∙ by Vincent Francois-Lavet, et al. ∙ 28 ∙ share

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Distributional reinforcement learning with linear function approximation

Despite many algorithmic advances, our theoretical understanding of prac...
02/08/2019 ∙ by Marc G. Bellemare, et al. ∙ 18 ∙ share

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Off-Policy Deep Reinforcement Learning by Bootstrapping the Covariate Shift

In this paper we revisit the method of off-policy corrections for reinfo...
01/27/2019 ∙ by Carles Gelada, et al. ∙ 14 ∙ share

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The Value Function Polytope in Reinforcement Learning

We establish geometric and topological properties of the space of value ...
01/31/2019 ∙ by Robert Dadashi, et al. ∙ 10 ∙ share

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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...
01/31/2019 ∙ by Marc G. Bellemare, et al. ∙ 10 ∙ share

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Shaping the Narrative Arc: An Information-Theoretic Approach to Collaborative Dialogue

We consider the problem of designing an artificial agent capable of inte...
01/31/2019 ∙ by Kory W. Mathewson, et al. ∙ 8 ∙ share

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A Comparative Analysis of Expected and Distributional Reinforcement Learning

Since their introduction a year ago, distributional approaches to reinfo...
01/30/2019 ∙ by Clare Lyle, et al. ∙ 6 ∙ share

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Count-Based Exploration with the Successor Representation

The problem of exploration in reinforcement learning is well-understood ...
07/31/2018 ∙ by Marlos C. Machado, et al. ∙ 4 ∙ share

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Hyperbolic Discounting and Learning over Multiple Horizons

Reinforcement learning (RL) typically defines a discount factor as part ...
02/19/2019 ∙ by William Fedus, et al. ∙ 4 ∙ share

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DeepMDP: Learning Continuous Latent Space Models for Representation Learning

Many reinforcement learning (RL) tasks provide the agent with high-dimen...
06/06/2019 ∙ by Carles Gelada, et al. ∙ 4 ∙ share

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Algorithmic Improvements for Deep Reinforcement Learning applied to Interactive Fiction

Text-based games are a natural challenge domain for deep reinforcement l...
11/28/2019 ∙ by Vishal Jain, et al. ∙ 3 ∙ share

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Approximate Exploration through State Abstraction

Although exploration in reinforcement learning is well understood from a...
08/29/2018 ∙ by Adrien Ali Taïga, et al. ∙ 2 ∙ share

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Benchmarking Bonus-Based Exploration Methods on the Arcade Learning Environment

This paper provides an empirical evaluation of recently developed explor...
08/06/2019 ∙ by Adrien Ali Taïga, et al. ∙ 2 ∙ share

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Dopamine: A Research Framework for Deep Reinforcement Learning

Deep reinforcement learning (deep RL) research has grown significantly i...
12/14/2018 ∙ by Pablo Samuel Castro, et al. ∙ 1 ∙ share

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Automated Curriculum Learning for Neural Networks

We introduce a method for automatically selecting the path, or syllabus,...
04/10/2017 ∙ by Alex Graves, et al. ∙ 0 ∙ share

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Distributional Reinforcement Learning with Quantile Regression

In reinforcement learning an agent interacts with the environment by tak...
10/27/2017 ∙ by Will Dabney, et al. ∙ 0 ∙ share

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A Distributional Perspective on Reinforcement Learning

In this paper we argue for the fundamental importance of the value distr...
07/21/2017 ∙ by Marc G. Bellemare, et al. ∙ 0 ∙ share

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The Reactor: A Sample-Efficient Actor-Critic Architecture

In this work we present a new reinforcement learning agent, called React...
04/15/2017 ∙ by Audrūnas Gruslys, et al. ∙ 0 ∙ share

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The Cramer Distance as a Solution to Biased Wasserstein Gradients

The Wasserstein probability metric has received much attention from the ...
05/30/2017 ∙ by Marc G. Bellemare, et al. ∙ 0 ∙ share

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Unifying Count-Based Exploration and Intrinsic Motivation

We consider an agent's uncertainty about its environment and the problem...
06/06/2016 ∙ by Marc G. Bellemare, et al. ∙ 0 ∙ share

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Increasing the Action Gap: New Operators for Reinforcement Learning

This paper introduces new optimality-preserving operators on Q-functions...
12/15/2015 ∙ by Marc G. Bellemare, et al. ∙ 0 ∙ share

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Safe and Efficient Off-Policy Reinforcement Learning

In this work, we take a fresh look at some old and new algorithms for of...
06/08/2016 ∙ by Remi Munos, et al. ∙ 0 ∙ share

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Compress and Control

This paper describes a new information-theoretic policy evaluation techn...
11/19/2014 ∙ by Joel Veness, et al. ∙ 0 ∙ share

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Q(λ) with Off-Policy Corrections

We propose and analyze an alternate approach to off-policy multi-step te...
02/16/2016 ∙ by Anna Harutyunyan, et al. ∙ 0 ∙ share

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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...
07/19/2012 ∙ by Marc G. Bellemare, et al. ∙ 0 ∙ share

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An Analysis of Categorical Distributional Reinforcement Learning

Distributional approaches to value-based reinforcement learning model th...
02/22/2018 ∙ by Mark Rowland, et al. ∙ 0 ∙ share

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Marc G. Bellemare

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