180 citations · 238 across the 5 of their papers we have counts for
5 papers
Provable Hierarchy-Based Meta-Reinforcement Learning
Kurtland Chua, Qi Lei, Jason D. Lee
Hierarchical reinforcement learning (HRL) has seen widespread interest as an approach to tractable learning of complex modular behaviors. However, existing work either assume acces…
How Fine-Tuning Allows for Effective Meta-Learning
Kurtland Chua, Qi Lei, Jason D. Lee
Representation learning has been widely studied in the context of meta-learning, enabling rapid learning of new tasks through shared representations. Recent works such as MAML have…
On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning
Baohe Zhang, Raghu Rajan, Luis Pineda +5
Model-based Reinforcement Learning (MBRL) is a promising framework for learning control in a data-efficient manner. MBRL algorithms can be fairly complex due to the separate dynami…
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
Kurtland Chua, Roberto Calandra, Rowan McAllister +1
Model-based reinforcement learning (RL) algorithms can attain excellent sample efficiency, but often lag behind the best model-free algorithms in terms of asymptotic performance. T…
MBMF: Model-Based Priors for Model-Free Reinforcement Learning
Somil Bansal, Roberto Calandra, Kurtland Chua +2
Reinforcement Learning is divided in two main paradigms: model-free and model-based. Each of these two paradigms has strengths and limitations, and has been successfully applied to…