Mikael Henaff | DeepAI

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∙ 06/05/2023

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

Exploration in environments which differ across episodes has received in...

0 Mikael Henaff, et al. ∙

∙ 10/12/2022

Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories

Natural agents can effectively learn from multiple data sources that dif...

0 Qinqing Zheng, et al. ∙

∙ 10/11/2022

Exploration via Elliptical Episodic Bonuses

In recent years, a number of reinforcement learning (RL) methods have be...

0 Mikael Henaff, et al. ∙

∙ 07/16/2020

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

Direct policy gradient methods for reinforcement learning are a successf...

0 Alekh Agarwal, et al. ∙

∙ 11/13/2019

Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning

We present an algorithm, HOMER, for exploration and reinforcement learni...

15 Dipendra Misra, et al. ∙

∙ 11/01/2019

Explicit Explore-Exploit Algorithms in Continuous State Spaces

We present a new model-based algorithm for reinforcement learning (RL) w...

0 Mikael Henaff, et al. ∙

∙ 01/08/2019

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

Learning a policy using only observational data is challenging because t...

6 Mikael Henaff, et al. ∙

∙ 11/14/2017

Prediction Under Uncertainty with Error-Encoding Networks

In this work we introduce a new framework for performing temporal predic...

0 Mikael Henaff, et al. ∙

∙ 05/19/2017

Model-Based Planning in Discrete Action Spaces

Planning actions using learned and differentiable forward models of the ...

0 Mikael Henaff, et al. ∙

∙ 12/12/2016

Tracking the World State with Recurrent Entity Networks

We introduce a new model, the Recurrent Entity Network (EntNet). It is e...

0 Mikael Henaff, et al. ∙

∙ 02/22/2016

Recurrent Orthogonal Networks and Long-Memory Tasks

Although RNNs have been shown to be powerful tools for processing sequen...

0 Mikael Henaff, et al. ∙

∙ 06/16/2015

Deep Convolutional Networks on Graph-Structured Data

Deep Learning's recent successes have mostly relied on Convolutional Net...

0 Mikael Henaff, et al. ∙

∙ 11/30/2014

The Loss Surfaces of Multilayer Networks

We study the connection between the highly non-convex loss function of a...

0 Anna Choromanska, et al. ∙

∙ 12/20/2013

Fast Training of Convolutional Networks through FFTs

Convolutional networks are one of the most widely employed architectures...

0 Michael Mathieu, et al. ∙

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