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Managing Action Preconditions in Neuro-Symbolic RL: Three Placement Strategies for Embodied Agents
Author1, Author2, Author3, Author4, Author5
neuro-symbolicreinforcement learningaction preconditionsembodied agents
2609.16056
Builder Relevance
1h ago80%
Abstract
This paper explores how neuro-symbolic reinforcement learning can integrate prior knowledge to improve agent performance in dynamic environments.
Reality Card
Core Claim
The symbolic enforcer placement in the RL loop significantly enhances solution quality, achieving 98.2% compared to the baseline's 88.8%.
Method / Result
The symbolic enforcer placement improved solution quality by 9.4% over the PPO+RND baseline.
Limitations
The experiments were conducted on specific benchmarks, which may not generalize to all environments.
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