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cs.LG2025
StaQ: a Finite Memory Approach to Discrete Action Policy Mirror Descent
Alex Davey, Alena Shilova, Brahim Driss +1
In Reinforcement Learning (RL), regularization with a Kullback-Leibler divergence that penalizes large deviations between successive policies has emerged as a popular tool both in…
cs.LG2025
Evaluating Interpretable Reinforcement Learning by Distilling Policies into Programs
Hector Kohler, Quentin Delfosse, Waris Radji +2
There exist applications of reinforcement learning like medicine where policies need to be ''interpretable'' by humans. User studies have shown that some policy classes might be mo…