Marc G. Bellemare

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Google ∙ University of Alberta

Research Scientist at Google Brain, Adjunct Professor at McGill University

Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning

Reinforcement learning methods trained on few environments rarely learn ...
01/13/2021 ∙ by Rishabh Agarwal, et al. ∙ 21 ∙ share

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The Importance of Pessimism in Fixed-Dataset Policy Optimization

We study worst-case guarantees on the expected return of fixed-dataset p...
09/15/2020 ∙ by Jacob Buckman, et al. ∙ 4 ∙ share

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Representations for Stable Off-Policy Reinforcement Learning

Reinforcement learning with function approximation can be unstable and e...
07/10/2020 ∙ by Dibya Ghosh, et al. ∙ 14 ∙ share

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A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms

We present a distributional approach to theoretical analyses of reinforc...
03/27/2020 ∙ by Philip Amortila, et al. ∙ 6 ∙ share

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Zooming for Efficient Model-Free Reinforcement Learning in Metric Spaces

Despite the wealth of research into provably efficient reinforcement lea...
03/09/2020 ∙ by Ahmed Touati, et al. ∙ 8 ∙ share

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On Catastrophic Interference in Atari 2600 Games

Model-free deep reinforcement learning algorithms are troubled with poor...
02/28/2020 ∙ by William Fedus, et al. ∙ 8 ∙ 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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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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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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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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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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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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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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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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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 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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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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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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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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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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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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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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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 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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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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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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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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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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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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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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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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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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Marc G. Bellemare

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