AGWM improves world model accuracy in compositional environments by learning an explicit DAG of action affordance prerequisites to handle dynamic executability.
International Conference on Learning Representations , year=
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Forager is a lightweight partially-observable continual RL environment that exposes loss of plasticity in current agents and highlights the value of state construction for ongoing learning.
citing papers explorer
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AGWM: Affordance-Grounded World Models for Environments with Compositional Prerequisites
AGWM improves world model accuracy in compositional environments by learning an explicit DAG of action affordance prerequisites to handle dynamic executability.
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Forager: a lightweight testbed for continual learning with partial observability in RL
Forager is a lightweight partially-observable continual RL environment that exposes loss of plasticity in current agents and highlights the value of state construction for ongoing learning.