1.4k citations · 2.8k across the 14 of their papers we have counts for
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cs.LG2019★ 14 cited
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…
cs.LG2019★ 13 cited
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…
cs.LG2019★ 78 cited
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…