73 citations · 324 across the 34 of their papers we have counts for
3 papers · 1 filter
Low Variance Off-policy Evaluation with State-based Importance Sampling
David M. Bossens, Philip S. Thomas
In many domains, the exploration process of reinforcement learning will be too costly as it requires trying out suboptimal policies, resulting in a need for off-policy evaluation,…
Enforcing Delayed-Impact Fairness Guarantees
Aline Weber, Blossom Metevier, Yuriy Brun +2
Recent research has shown that seemingly fair machine learning models, when used to inform decisions that have an impact on peoples' lives or well-being (e.g., applications involvi…
Adaptive Rollout Length for Model-Based RL Using Model-Free Deep RL
Abhinav Bhatia, Philip S. Thomas, Shlomo Zilberstein
Model-based reinforcement learning promises to learn an optimal policy from fewer interactions with the environment compared to model-free reinforcement learning by learning an int…