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cs.LG2025
Surrogate Fitness Metrics for Interpretable Reinforcement Learning
Philipp Altmann, Céline Davignon, Maximilian Zorn +3
We employ an evolutionary optimization framework that perturbs initial states to generate informative and diverse policy demonstrations. A joint surrogate fitness function guides t…
cs.LG2024
REACT: Revealing Evolutionary Action Consequence Trajectories for Interpretable Reinforcement Learning
Philipp Altmann, Céline Davignon, Maximilian Zorn +3
To enhance the interpretability of Reinforcement Learning (RL), we propose Revealing Evolutionary Action Consequence Trajectories (REACT). In contrast to the prevalent practice of…