3 citations · 6 across the 7 of their papers we have counts for
6 papers · 1 filter
Context-Action Embedding Learning for Off-Policy Evaluation in Contextual Bandits
Kushagra Chandak, Vincent Liu, Haanvid Lee
We consider off-policy evaluation (OPE) in contextual bandits with finite action space. Inverse Propensity Score (IPS) weighting is a widely used method for OPE due to its unbiased…
No More Pesky Hyperparameters: Offline Hyperparameter Tuning for RL
Han Wang, Archit Sakhadeo, Adam White +7
The performance of reinforcement learning (RL) agents is sensitive to the choice of hyperparameters. In real-world settings like robotics or industrial control systems, however, te…
Towards a practical measure of interference for reinforcement learning
Vincent Liu, Adam White, Hengshuai Yao +1
Catastrophic interference is common in many network-based learning systems, and many proposals exist for mitigating it. But, before we overcome interference we must understand it b…
Incrementally Learning Functions of the Return
Brendan Bennett, Wesley Chung, Muhammad Zaheer +1
Temporal difference methods enable efficient estimation of value functions in reinforcement learning in an incremental fashion, and are of broader interest because they correspond…
Recurrent Control Nets for Deep Reinforcement Learning
Vincent Liu, Ademi Adeniji, Nathaniel Lee +2
Central Pattern Generators (CPGs) are biological neural circuits capable of producing coordinated rhythmic outputs in the absence of rhythmic input. As a result, they are responsib…
The Utility of Sparse Representations for Control in Reinforcement Learning
Vincent Liu, Raksha Kumaraswamy, Lei Le +1
We investigate sparse representations for control in reinforcement learning. While these representations are widely used in computer vision, their prevalence in reinforcement learn…