Bilal Piot | DeepAI

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∙ 05/02/2023

Unlocking the Power of Representations in Long-term Novelty-based Exploration

We introduce Robust Exploration via Clustering-based Online Density Esti...

0 Alaa Saade, et al. ∙

∙ 02/09/2023

The Edge of Orthogonality: A Simple View of What Makes BYOL Tick

Self-predictive unsupervised learning methods such as BYOL or SimSiam ha...

0 Pierre H. Richemond, et al. ∙

∙ 12/06/2022

Understanding Self-Predictive Learning for Reinforcement Learning

We study the learning dynamics of self-predictive learning for reinforce...

0 Yunhao Tang, et al. ∙

∙ 06/30/2022

Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning

We introduce DeepNash, an autonomous agent capable of learning to play t...

6 Julien Perolat, et al. ∙

∙ 06/16/2022

BYOL-Explore: Exploration by Bootstrapped Prediction

We present BYOL-Explore, a conceptually simple yet general approach for ...

0 Zhaohan Daniel Guo, et al. ∙

∙ 10/20/2021

Shaking the foundations: delusions in sequence models for interaction and control

The recent phenomenal success of language models has reinvigorated machi...

68 Pedro A. Ortega, et al. ∙

∙ 01/06/2021

Geometric Entropic Exploration

Exploration is essential for solving complex Reinforcement Learning (RL)...

0 Zhaohan Daniel Guo, et al. ∙

∙ 10/20/2020

BYOL works even without batch statistics

Bootstrap Your Own Latent (BYOL) is a self-supervised learning approach ...

0 Pierre H. Richemond, et al. ∙

∙ 06/13/2020

Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning

We introduce Bootstrap Your Own Latent (BYOL), a new approach to self-su...

0 Jean-Bastien Grill, et al. ∙

∙ 06/01/2020

Acme: A Research Framework for Distributed Reinforcement Learning

Deep reinforcement learning has led to many recent-and groundbreaking-ad...

22 Matt Hoffman, et al. ∙

∙ 04/30/2020

Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning

Learning a good representation is an essential component for deep reinfo...

0 Daniel Guo, et al. ∙

∙ 03/30/2020

Agent57: Outperforming the Atari Human Benchmark

Atari games have been a long-standing benchmark in the reinforcement lea...

9 Adrià Puigdomènech Badia, et al. ∙

∙ 02/14/2020

Never Give Up: Learning Directed Exploration Strategies

We propose a reinforcement learning agent to solve hard exploration game...

7 Adrià Puigdomènech Badia, et al. ∙

∙ 12/05/2019

Hindsight Credit Assignment

We consider the problem of efficient credit assignment in reinforcement ...

0 Anna Harutyunyan, et al. ∙

∙ 02/20/2019

World Discovery Models

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

0 Mohammad Gheshlaghi Azar, et al. ∙

∙ 11/15/2018

Neural Predictive Belief Representations

Unsupervised representation learning has succeeded with excellent result...

0 Zhaohan Daniel Guo, et al. ∙

∙ 09/20/2018

Playing the Game of Universal Adversarial Perturbations

We study the problem of learning classifiers robust to universal adversa...

12 Julien Perolat, et al. ∙

∙ 05/29/2018

Observe and Look Further: Achieving Consistent Performance on Atari

Despite significant advances in the field of deep Reinforcement Learning...

0 Tobias Pohlen, et al. ∙

∙ 10/06/2017

Rainbow: Combining Improvements in Deep Reinforcement Learning

The deep reinforcement learning community has made several independent i...

0 Matteo Hessel, et al. ∙

∙ 07/27/2017

Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards

We propose a general and model-free approach for Reinforcement Learning ...

0 Matej Večerík, et al. ∙

∙ 06/30/2017

Noisy Networks for Exploration

We introduce NoisyNet, a deep reinforcement learning agent with parametr...

0 Meire Fortunato, et al. ∙

∙ 06/20/2017

Observational Learning by Reinforcement Learning

Observational learning is a type of learning that occurs as a function o...

0 Diana Borsa, et al. ∙

∙ 04/12/2017

Deep Q-learning from Demonstrations

Deep reinforcement learning (RL) has achieved several high profile succe...

0 Todd Hester, et al. ∙

∙ 03/15/2017

End-to-end optimization of goal-driven and visually grounded dialogue systems

End-to-end design of dialogue systems has recently become a popular rese...

0 Florian Strub, et al. ∙

∙ 06/24/2016

Is the Bellman residual a bad proxy?

This paper aims at theoretically and empirically comparing two standard ...

0 Matthieu Geist, et al. ∙

∙ 06/03/2016

Difference of Convex Functions Programming Applied to Control with Expert Data

This paper reports applications of Difference of Convex functions (DC) p...

0 Bilal Piot, et al. ∙

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