1 citations · 1 across the 9 of their papers we have counts for
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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…
SocialGrid: A Benchmark for Planning and Social Reasoning in Embodied Multi-Agent Systems
Hikaru Shindo, Hanzhao Lin, Lukas Helff +2
As Large Language Models (LLMs) transition from text processors to autonomous agents, evaluating their social reasoning in embodied multi-agent settings becomes critical. We introd…
Learning from Less: Guiding Deep Reinforcement Learning with Differentiable Symbolic Planning
Zihan Ye, Oleg Arenz, Kristian Kersting
When tackling complex problems, humans naturally break them down into smaller, manageable subtasks and adjust their initial plans based on observations. For instance, if you want t…
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…