Search Results for author: Mikael Henaff

Found 19 papers, 15 papers with code

Generalization to New Sequential Decision Making Tasks with In-Context Learning

1 code implementation6 Dec 2023 Sharath Chandra Raparthy, Eric Hambro, Robert Kirk, Mikael Henaff, Roberta Raileanu

By training on large diverse offline datasets, our model is able to learn new MiniHack and Procgen tasks without any weight updates from just a handful of demonstrations.

Decision Making In-Context Learning

A Study of Global and Episodic Bonuses for Exploration in Contextual MDPs

2 code implementations5 Jun 2023 Mikael Henaff, Minqi Jiang, Roberta Raileanu

This results in an algorithm which sets a new state of the art across 16 tasks from the MiniHack suite used in prior work, and also performs robustly on Habitat and Montezuma's Revenge.

Montezuma's Revenge

Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories

1 code implementation12 Oct 2022 Qinqing Zheng, Mikael Henaff, Brandon Amos, Aditya Grover

For this setting, we develop and study a simple meta-algorithmic pipeline that learns an inverse dynamics model on the labelled data to obtain proxy-labels for the unlabelled data, followed by the use of any offline RL algorithm on the true and proxy-labelled trajectories.

D4RL Offline RL +2

Exploration via Elliptical Episodic Bonuses

2 code implementations11 Oct 2022 Mikael Henaff, Roberta Raileanu, Minqi Jiang, Tim Rocktäschel

In recent years, a number of reinforcement learning (RL) methods have been proposed to explore complex environments which differ across episodes.

Reinforcement Learning (RL)

PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning

1 code implementation NeurIPS 2020 Alekh Agarwal, Mikael Henaff, Sham Kakade, Wen Sun

Direct policy gradient methods for reinforcement learning are a successful approach for a variety of reasons: they are model free, they directly optimize the performance metric of interest, and they allow for richly parameterized policies.

Policy Gradient Methods Q-Learning

Disagreement-Regularized Imitation Learning

2 code implementations ICLR 2020 Kiante Brantley, Wen Sun, Mikael Henaff

We present a simple and effective algorithm designed to address the covariate shift problem in imitation learning.

Continuous Control Imitation Learning

Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning

no code implementations ICML 2020 Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, John Langford

We present an algorithm, HOMER, for exploration and reinforcement learning in rich observation environments that are summarizable by an unknown latent state space.

reinforcement-learning Reinforcement Learning (RL) +1

Explicit Explore-Exploit Algorithms in Continuous State Spaces

1 code implementation NeurIPS 2019 Mikael Henaff

We present a new model-based algorithm for reinforcement learning (RL) which consists of explicit exploration and exploitation phases, and is applicable in large or infinite state spaces.

Reinforcement Learning (RL)

Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Traffic

1 code implementation ICLR 2019 Mikael Henaff, Alfredo Canziani, Yann Lecun

Learning a policy using only observational data is challenging because the distribution of states it induces at execution time may differ from the distribution observed during training.

Rolling Shutter Correction

Prediction Under Uncertainty with Error Encoding Networks

no code implementations ICLR 2018 Mikael Henaff, Junbo Zhao, Yann Lecun

In this work we introduce a new framework for performing temporal predictions in the presence of uncertainty.

Video Prediction

Prediction Under Uncertainty with Error-Encoding Networks

2 code implementations14 Nov 2017 Mikael Henaff, Junbo Zhao, Yann Lecun

In this work we introduce a new framework for performing temporal predictions in the presence of uncertainty.

Video Prediction

Model-Based Planning with Discrete and Continuous Actions

1 code implementation19 May 2017 Mikael Henaff, William F. Whitney, Yann Lecun

Action planning using learned and differentiable forward models of the world is a general approach which has a number of desirable properties, including improved sample complexity over model-free RL methods, reuse of learned models across different tasks, and the ability to perform efficient gradient-based optimization in continuous action spaces.

Tracking the World State with Recurrent Entity Networks

5 code implementations12 Dec 2016 Mikael Henaff, Jason Weston, Arthur Szlam, Antoine Bordes, Yann Lecun

The EntNet sets a new state-of-the-art on the bAbI tasks, and is the first method to solve all the tasks in the 10k training examples setting.

Procedural Text Understanding Question Answering +1

Recurrent Orthogonal Networks and Long-Memory Tasks

1 code implementation22 Feb 2016 Mikael Henaff, Arthur Szlam, Yann Lecun

Although RNNs have been shown to be powerful tools for processing sequential data, finding architectures or optimization strategies that allow them to model very long term dependencies is still an active area of research.

Deep Convolutional Networks on Graph-Structured Data

3 code implementations16 Jun 2015 Mikael Henaff, Joan Bruna, Yann Lecun

Deep Learning's recent successes have mostly relied on Convolutional Networks, which exploit fundamental statistical properties of images, sounds and video data: the local stationarity and multi-scale compositional structure, that allows expressing long range interactions in terms of shorter, localized interactions.

General Classification Test

The Loss Surfaces of Multilayer Networks

1 code implementation30 Nov 2014 Anna Choromanska, Mikael Henaff, Michael Mathieu, Gérard Ben Arous, Yann Lecun

We show that for large-size decoupled networks the lowest critical values of the random loss function form a layered structure and they are located in a well-defined band lower-bounded by the global minimum.


Fast Training of Convolutional Networks through FFTs

no code implementations20 Dec 2013 Michael Mathieu, Mikael Henaff, Yann Lecun

Convolutional networks are one of the most widely employed architectures in computer vision and machine learning.

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