Search Results for author:
Found 2 papers, 1 papers with code
Rethinking Out-of-Distribution Detection for Reinforcement Learning: Advancing Methods for Evaluation and Detection
In this paper, we study the problem of out-of-distribution (OOD) detection in RL, which focuses on identifying situations at test time that RL agents have not encountered in their training environments.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
+2
Trust and Time Preference: Measuring a Causal Effect in a Random-Assignment Experiment
Following Gabaix and Laibson (2017), we first argue that time preference can be modelled as optimal Bayesian inference based on noisy signals about the future, so that it is affected by the perceived certainty of future outcomes.
