1.4k citations · 1.5k across the 6 of their papers we have counts for
6 papers
PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning
Alekh Agarwal, Mikael Henaff, Sham Kakade +1
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…
Explicit Explore-Exploit Algorithms in Continuous State Spaces
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…
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning
Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy +1
We present an algorithm, HOMER, for exploration and reinforcement learning in rich observation environments that are summarizable by an unknown latent state space. The algorithm in…
Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Traffic
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 trai…
Prediction Under Uncertainty with Error-Encoding Networks
Mikael Henaff, Junbo Zhao, Yann LeCun
In this work we introduce a new framework for performing temporal predictions in the presence of uncertainty. It is based on a simple idea of disentangling components of the future…
Deep Convolutional Networks on Graph-Structured Data
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 stationa…