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cs.AI2024
EXPIL: Explanatory Predicate Invention for Learning in Games
Jingyuan Sha, Hikaru Shindo, Quentin Delfosse +2
Reinforcement learning (RL) has proven to be a powerful tool for training agents that excel in various games. However, the black-box nature of neural network models often hinders o…
cs.AI2024
HackAtari: Atari Learning Environments for Robust and Continual Reinforcement Learning
Quentin Delfosse, Jannis Blüml, Bjarne Gregori +1
Artificial agents' adaptability to novelty and alignment with intended behavior is crucial for their effective deployment. Reinforcement learning (RL) leverages novelty as a means…
cs.AI2024
Towards a Research Community in Interpretable Reinforcement Learning: the InterpPol Workshop
Hector Kohler, Quentin Delfosse, Paul Festor +1
Embracing the pursuit of intrinsically explainable reinforcement learning raises crucial questions: what distinguishes explainability from interpretability? Should explainable and…