3.3k citations · 8.2k across the 53 of their papers we have counts for
5 papers · 1 filter
Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives
George Tucker, Dieterich Lawson, Shixiang Gu +1
Deep latent variable models have become a popular model choice due to the scalable learning algorithms introduced by (Kingma & Welling, 2013; Rezende et al., 2014). These approache…
Near-Optimal Representation Learning for Hierarchical Reinforcement Learning
Ofir Nachum, Shixiang Gu, Honglak Lee +1
We study the problem of representation learning in goal-conditioned hierarchical reinforcement learning. In such hierarchical structures, a higher-level controller solves tasks by…
Data-Efficient Hierarchical Reinforcement Learning
Ofir Nachum, Shixiang Gu, Honglak Lee +1
Hierarchical reinforcement learning (HRL) is a promising approach to extend traditional reinforcement learning (RL) methods to solve more complex tasks. Yet, the majority of curren…
The Mirage of Action-Dependent Baselines in Reinforcement Learning
George Tucker, Surya Bhupatiraju, Shixiang Gu +3
Policy gradient methods are a widely used class of model-free reinforcement learning algorithms where a state-dependent baseline is used to reduce gradient estimator variance. Seve…
Temporal Difference Models: Model-Free Deep RL for Model-Based Control
Vitchyr Pong, Shixiang Gu, Murtaza Dalal +1
Model-free reinforcement learning (RL) is a powerful, general tool for learning complex behaviors. However, its sample efficiency is often impractically large for solving challengi…