3 citations · 3 across the 19 of their papers we have counts for
9 papers · 1 filter
GRAIL: Autonomous Concept Grounding for Neuro-Symbolic Reinforcement Learning
Hikaru Shindo, Henri Rößler, Quentin Delfosse +1
Neuro-symbolic Reinforcement Learning (NeSy-RL) combines symbolic reasoning with gradient-based optimization to achieve interpretable and generalizable policies. Relational concept…
Boosting deep Reinforcement Learning using pretraining with Logical Options
Zihan Ye, Phil Chau, Raban Emunds +5
Deep reinforcement learning agents are often misaligned, as they over-exploit early reward signals. Recently, several symbolic approaches have addressed these challenges by encodin…
Better Decisions through the Right Causal World Model
Elisabeth Dillies, Quentin Delfosse, Jannis Blüml +3
Reinforcement learning (RL) agents have shown remarkable performances in various environments, where they can discover effective policies directly from sensory inputs. However, the…
Interpretable end-to-end Neurosymbolic Reinforcement Learning agents
Nils Grandien, Quentin Delfosse, Kristian Kersting
Deep reinforcement learning (RL) agents rely on shortcut learning, preventing them from generalizing to slightly different environments. To address this problem, symbolic method, t…
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…
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…