Remi Munos

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Google ∙ Inria

Senior Researcher at Google Deepmind

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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Taylor Expansion Policy Optimization

In this work, we investigate the application of Taylor expansions in rei...
03/13/2020 ∙ by Yunhao Tang, et al. ∙ 36 ∙ share

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From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization

In this paper we investigate the Follow the Regularized Leader dynamics ...
02/19/2020 ∙ by Julien Perolat, et al. ∙ 32 ∙ share

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Multiagent Evaluation under Incomplete Information

This paper investigates the evaluation of learned multiagent strategies ...
09/21/2019 ∙ by Mark Rowland, et al. ∙ 24 ∙ share

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A Generalized Training Approach for Multiagent Learning

This paper investigates a population-based training regime based on game...
09/27/2019 ∙ by Paul Müller, et al. ∙ 20 ∙ share

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Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement

The ability to transfer skills across tasks has the potential to scale u...
01/30/2019 ∙ by Andre Barreto, et al. ∙ 12 ∙ share

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Neural Replicator Dynamics

In multiagent learning, agents interact in inherently nonstationary envi...
06/01/2019 ∙ by Shayegan Omidshafiei, et al. ∙ 12 ∙ share

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Conditional Importance Sampling for Off-Policy Learning

The principal contribution of this paper is a conceptual framework for o...
10/16/2019 ∙ by Mark Rowland, et al. ∙ 12 ∙ share

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The Termination Critic

In this work, we consider the problem of autonomously discovering behavi...
02/26/2019 ∙ by Anna Harutyunyan, et al. ∙ 10 ∙ share

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Actor-Critic Policy Optimization in Partially Observable Multiagent Environments

Optimization of parameterized policies for reinforcement learning (RL) i...
10/21/2018 ∙ by Sriram Srinivasan, et al. ∙ 8 ∙ share

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Universal Successor Features Approximators

The ability of a reinforcement learning (RL) agent to learn about many r...
12/18/2018 ∙ by Diana Borsa, et al. ∙ 6 ∙ 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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The Uncertainty Bellman Equation and Exploration

We consider the exploration/exploitation problem in reinforcement learni...
09/15/2017 ∙ by Brendan O'Donoghue, et al. ∙ 0 ∙ share

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Memory-Efficient Backpropagation Through Time

We propose a novel approach to reduce memory consumption of the backprop...
06/10/2016 ∙ by Audrūnas Gruslys, 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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Noisy Networks for Exploration

We introduce NoisyNet, a deep reinforcement learning agent with parametr...
06/30/2017 ∙ by Meire Fortunato, et al. ∙ 0 ∙ share

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Observational Learning by Reinforcement Learning

Observational learning is a type of learning that occurs as a function o...
06/20/2017 ∙ by Diana Borsa, 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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Minimax Regret Bounds for Reinforcement Learning

We consider the problem of provably optimal exploration in reinforcement...
03/16/2017 ∙ by Mohammad Gheshlaghi Azar, et al. ∙ 0 ∙ share

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Successor Features for Transfer in Reinforcement Learning

Transfer in reinforcement learning refers to the notion that generalizat...
06/16/2016 ∙ by Andre Barreto, 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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Learning to reinforcement learn

In recent years deep reinforcement learning (RL) systems have attained s...
11/17/2016 ∙ by Jane X Wang, et al. ∙ 0 ∙ share

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Combining policy gradient and Q-learning

Policy gradient is an efficient technique for improving a policy in a re...
11/05/2016 ∙ by Brendan O'Donoghue, 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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On Minimax Optimal Offline Policy Evaluation

This paper studies the off-policy evaluation problem, where one aims to ...
09/12/2014 ∙ by Lihong Li, et al. ∙ 0 ∙ share

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Bandit Algorithms for Tree Search

Bandit based methods for tree search have recently gained popularity whe...
08/09/2014 ∙ by Pierre-Arnuad Coquelin, 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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Generalized Emphatic Temporal Difference Learning: Bias-Variance Analysis

We consider the off-policy evaluation problem in Markov decision process...
09/17/2015 ∙ by Assaf Hallak, et al. ∙ 0 ∙ share

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Active Regression by Stratification

We propose a new active learning algorithm for parametric linear regress...
10/22/2014 ∙ by Sivan Sabato, et al. ∙ 0 ∙ share

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Finite-Time Analysis of Kernelised Contextual Bandits

We tackle the problem of online reward maximisation over a large finite ...
09/26/2013 ∙ by Michal Valko, et al. ∙ 0 ∙ share

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Thompson Sampling for 1-Dimensional Exponential Family Bandits

Thompson Sampling has been demonstrated in many complex bandit models, h...
07/12/2013 ∙ by Nathaniel Korda, et al. ∙ 0 ∙ share

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Fast gradient descent for drifting least squares regression, with application to bandits

Online learning algorithms require to often recompute least squares regr...
07/11/2013 ∙ by Nathaniel Korda, et al. ∙ 0 ∙ share

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Stochastic approximation for speeding up LSTD (and LSPI)

We propose a stochastic approximation (SA) based method with randomizati...
06/11/2013 ∙ by L. A. Prashanth, et al. ∙ 0 ∙ share

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Toward Optimal Stratification for Stratified Monte-Carlo Integration

We consider the problem of adaptive stratified sampling for Monte Carlo ...
03/12/2013 ∙ by Alexandra Carpentier, et al. ∙ 0 ∙ share

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Adaptive Stratified Sampling for Monte-Carlo integration of Differentiable functions

We consider the problem of adaptive stratified sampling for Monte Carlo ...
10/19/2012 ∙ by Alexandra Carpentier, et al. ∙ 0 ∙ share

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Regret Bounds for Restless Markov Bandits

We consider the restless Markov bandit problem, in which the state of ea...
09/12/2012 ∙ by Ronald Ortner, et al. ∙ 0 ∙ share

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On the Sample Complexity of Reinforcement Learning with a Generative Model

We consider the problem of learning the optimal action-value function in...
06/27/2012 ∙ by Mohammad Gheshlaghi Azar, et al. ∙ 0 ∙ share

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Thompson Sampling: An Asymptotically Optimal Finite Time Analysis

The question of the optimality of Thompson Sampling for solving the stoc...
05/18/2012 ∙ by Emilie Kaufmann, et al. ∙ 0 ∙ share

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IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures

In this work we aim to solve a large collection of tasks using a single ...
02/05/2018 ∙ by Lasse Espeholt, 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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Learning to Search with MCTSnets

Planning problems are among the most important and well-studied problems...
02/13/2018 ∙ by Arthur Guez, et al. ∙ 0 ∙ share

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A Study on Overfitting in Deep Reinforcement Learning

Recent years have witnessed significant progresses in deep Reinforcement...
04/18/2018 ∙ by Chiyuan Zhang, et al. ∙ 0 ∙ share

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Low-pass Recurrent Neural Networks - A memory architecture for longer-term correlation discovery

Reinforcement learning (RL) agents performing complex tasks must be able...
05/13/2018 ∙ by Thomas Stepleton, et al. ∙ 0 ∙ share

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Observe and Look Further: Achieving Consistent Performance on Atari

Despite significant advances in the field of deep Reinforcement Learning...
05/29/2018 ∙ by Tobias Pohlen, et al. ∙ 0 ∙ share

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Autoregressive Quantile Networks for Generative Modeling

We introduce autoregressive implicit quantile networks (AIQN), a fundame...
06/14/2018 ∙ by Georg Ostrovski, et al. ∙ 0 ∙ share

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Implicit Quantile Networks for Distributional Reinforcement Learning

In this work, we build on recent advances in distributional reinforcemen...
06/14/2018 ∙ by Will Dabney, et al. ∙ 0 ∙ share

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Maximum a Posteriori Policy Optimisation

We introduce a new algorithm for reinforcement learning called Maximum a...
06/14/2018 ∙ by Abbas Abdolmaleki, et al. ∙ 0 ∙ share

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Remi Munos

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