Search Results for author: Mohammad Gheshlaghi Azar

Found 25 papers, 8 papers with code

Fast computation of Nash Equilibria in Imperfect Information Games

no code implementations ICML 2020 Remi Munos, Julien Perolat, Jean-Baptiste Lespiau, Mark Rowland, Bart De Vylder, Marc Lanctot, Finbarr Timbers, Daniel Hennes, Shayegan Omidshafiei, Audrunas Gruslys, Mohammad Gheshlaghi Azar, Edward Lockhart, Karl Tuyls

We introduce and analyze a class of algorithms, called Mirror Ascent against an Improved Opponent (MAIO), for computing Nash equilibria in two-player zero-sum games, both in normal form and in sequential imperfect information form.

An Analysis of Quantile Temporal-Difference Learning

no code implementations11 Jan 2023 Mark Rowland, Rémi Munos, Mohammad Gheshlaghi Azar, Yunhao Tang, Georg Ostrovski, Anna Harutyunyan, Karl Tuyls, Marc G. Bellemare, Will Dabney

We analyse quantile temporal-difference learning (QTD), a distributional reinforcement learning algorithm that has proven to be a key component in several successful large-scale applications of reinforcement learning.

Distributional Reinforcement Learning reinforcement-learning +1

Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity

1 code implementation NeurIPS 2021 Ran Liu, Mehdi Azabou, Max Dabagia, Chi-Heng Lin, Mohammad Gheshlaghi Azar, Keith B. Hengen, Michal Valko, Eva L. Dyer

Our approach combines a generative modeling framework with an instance-specific alignment loss that tries to maximize the representational similarity between transformed views of the input (brain state).

Large-Scale Representation Learning on Graphs via Bootstrapping

3 code implementations ICLR 2022 Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Mehdi Azabou, Eva L. Dyer, Rémi Munos, Petar Veličković, Michal Valko

To address these challenges, we introduce Bootstrapped Graph Latents (BGRL) - a graph representation learning method that learns by predicting alternative augmentations of the input.

Contrastive Learning Graph Representation Learning +1

Towards Consistent Performance on Atari using Expert Demonstrations

no code implementations ICLR 2019 Tobias Pohlen, Bilal Piot, Todd Hester, Mohammad Gheshlaghi Azar, Dan Horgan, David Budden, Gabriel Barth-Maron, Hado van Hasselt, John Quan, Mel Večerík, Matteo Hessel, Rémi Munos, Olivier Pietquin

Despite significant advances in the field of deep Reinforcement Learning (RL), today's algorithms still fail to learn human-level policies consistently over a set of diverse tasks such as Atari 2600 games.

Atari Games

World Discovery Models

1 code implementation20 Feb 2019 Mohammad Gheshlaghi Azar, Bilal Piot, Bernardo Avila Pires, Jean-bastien Grill, Florent Altché, Rémi Munos

As humans we are driven by a strong desire for seeking novelty in our world.

Neural Predictive Belief Representations

no code implementations15 Nov 2018 Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Bernardo A. Pires, Rémi Munos

In partially observable domains it is important for the representation to encode a belief state, a sufficient statistic of the observations seen so far.

Decision Making Representation Learning

Observe and Look Further: Achieving Consistent Performance on Atari

no code implementations29 May 2018 Tobias Pohlen, Bilal Piot, Todd Hester, Mohammad Gheshlaghi Azar, Dan Horgan, David Budden, Gabriel Barth-Maron, Hado van Hasselt, John Quan, Mel Večerík, Matteo Hessel, Rémi Munos, Olivier Pietquin

Despite significant advances in the field of deep Reinforcement Learning (RL), today's algorithms still fail to learn human-level policies consistently over a set of diverse tasks such as Atari 2600 games.

Montezuma's Revenge

Noisy Networks for Exploration

15 code implementations ICLR 2018 Meire Fortunato, Mohammad Gheshlaghi Azar, Bilal Piot, Jacob Menick, Ian Osband, Alex Graves, Vlad Mnih, Remi Munos, Demis Hassabis, Olivier Pietquin, Charles Blundell, Shane Legg

We introduce NoisyNet, a deep reinforcement learning agent with parametric noise added to its weights, and show that the induced stochasticity of the agent's policy can be used to aid efficient exploration.

Atari Games Efficient Exploration +2

The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning

no code implementations ICLR 2018 Audrunas Gruslys, Will Dabney, Mohammad Gheshlaghi Azar, Bilal Piot, Marc Bellemare, Remi Munos

Our first contribution is a new policy evaluation algorithm called Distributional Retrace, which brings multi-step off-policy updates to the distributional reinforcement learning setting.

Atari Games Distributional Reinforcement Learning +1

Convex Relaxation Regression: Black-Box Optimization of Smooth Functions by Learning Their Convex Envelopes

no code implementations5 Feb 2016 Mohammad Gheshlaghi Azar, Eva Dyer, Konrad Kording

Our approach enables the use of convex optimization tools to solve a class of non-convex optimization problems.

regression

Online Stochastic Optimization under Correlated Bandit Feedback

no code implementations4 Feb 2014 Mohammad Gheshlaghi Azar, Alessandro Lazaric, Emma Brunskill

In this paper we consider the problem of online stochastic optimization of a locally smooth function under bandit feedback.

Stochastic Optimization

Sequential Transfer in Multi-armed Bandit with Finite Set of Models

no code implementations NeurIPS 2013 Mohammad Gheshlaghi Azar, Alessandro Lazaric, Emma Brunskill

Learning from prior tasks and transferring that experience to improve future performance is critical for building lifelong learning agents.

reinforcement-learning reinforcement Learning

Regret Bounds for Reinforcement Learning with Policy Advice

no code implementations5 May 2013 Mohammad Gheshlaghi Azar, Alessandro Lazaric, Emma Brunskill

In some reinforcement learning problems an agent may be provided with a set of input policies, perhaps learned from prior experience or provided by advisors.

reinforcement-learning reinforcement Learning

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