8 citations · 18 across the 14 of their papers we have counts for
3 papers · 1 filter
When is Offline Policy Selection Sample Efficient for Reinforcement Learning?
Vincent Liu, Prabhat Nagarajan, Andrew Patterson +1
Offline reinforcement learning algorithms often require careful hyperparameter tuning. Before deployment, we need to select amongst a set of candidate policies. However, there is l…
Measuring and Mitigating Interference in Reinforcement Learning
Vincent Liu, Han Wang, Ruo Yu Tao +3
Catastrophic interference is common in many network-based learning systems, and many proposals exist for mitigating it. Before overcoming interference we must understand it better.…
Asymptotically Unbiased Off-Policy Policy Evaluation when Reusing Old Data in Nonstationary Environments
Vincent Liu, Yash Chandak, Philip Thomas +1
In this work, we consider the off-policy policy evaluation problem for contextual bandits and finite horizon reinforcement learning in the nonstationary setting. Reusing old data i…