[Submitted on 13 Sep 2026]
Title:Managing Action Preconditions in Neuro-Symbolic RL: Three Placement Strategies for Embodied Agents
View a PDF of the paper titled Managing Action Preconditions in Neuro-Symbolic RL: Three Placement Strategies for Embodied Agents, by Norbert Oswald and 1 other authors
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Abstract:Humans carry behaviour knowledge of how to act in familiar situations into every new task rather than relearning it from scratch. There is no reason a Reinforcement Learning (RL) agent shouldn't do the same: known behaviour patterns need not be learned, only applied. Neuro-symbolic RL bridges prior knowledge and RL by injecting symbolic knowledge alongside a learned policy. The point at which this knowledge is integrated is critical: a poor choice can produce, for instance, hallucinated preconditions, which surface as safety and reliability problems in agents acting in changing environments. We formalise this behavioural knowledge as a precondition Bayesian network (BN) over the agent's \emph{structural actions} - the actions whose legality depends on preconditions, such as picking up a key, grasping a block, toggling a door, or dropping an object. The BN restricts when these actions may fire, and we inject it into the RL loop at three placements: (1) a \emph{symbolic verifier}, consulted only at inference, that fires a structural action once its preconditions hold; (2) a \emph{symbolic enforcer}, active during both training and inference, that governs structural-action use throughout learning; and (3) a \emph{symbolic learner}, which folds the knowledge into the network and learns the restriction and use of structural actions itself. To test the three variants we run experiments on two benchmarks with opposite regimes: one built on long, ordered planning chains, the other on continuous manipulation. We compare against strong baselines on solution quality, sample efficiency, and traceability. The payoff is substantial. On MiniGrid, all three placements improve the \emph{solution quality} over the PPO+RND baseline, the symbolic enforcer leading at $98.2\%$ against the baseline's $88.8\%$. On Fetch, $\dots$
Comments: 10 pages, 4 figures and 2 tables
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2609.16056 [cs.LG]
(or arXiv:2609.16056v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.16056
arXiv-issued DOI via DataCite
Submission history
From: Norbert Oswald [view email] [v1] Sun, 13 Sep 2026 06:05:11 UTC (1,528 KB)
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