Managing Action Preconditions in Neuro-Symbolic RL: Three Placement Strategies for Embodied Agents

arXiv:2609.16056 · cs.LG, cs.AI · Submitted 2026-09-13 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-09-13

Updated: 2026-09-13

Comments: 10 pages, 4 figures and 2 tables

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Reinforcement Learning — 18× in this paper · explained in 4 episodes
• Benchmark — 13× in this paper · explained in 2 episodes
• Sample Efficiency — 9× in this paper · explained in 4 episodes
• Symbolic Enforcer — 8× in this paper
• Verifier — 7× in this paper · explained in 2 episodes
• Action Space — 6× in this paper · explained in 1 episode
• Symbolic Verifier — 6× in this paper
• Symbolic Learner — 4× in this paper
• Actor — 3× in this paper · explained in 3 episodes
• Bayesian Network (BN) — 3× in this paper · explained in 2 episodes
• Abstraction — 2× in this paper · explained in 1 episode
• Deep Reinforcement Learning — 2× in this paper · explained in 2 episodes
• Distillation — 2× in this paper · explained in 2 episodes
• Reward Shaping — 2× in this paper · explained in 2 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Affordance — 1× in this paper · explained in 1 episode
• Formal Methods — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Markov Decision Process (MDP) — 1× in this paper · explained in 11 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Reward Function — 1× in this paper · explained in 2 episodes
• Temporal Abstraction — 1× in this paper · explained in 2 episodes
• World Model — 1× in this paper · explained in 7 episodes
• Zero-Shot Generalization — 1× in this paper · explained in 5 episodes
• Zero-Shot Transfer — 1× in this paper · explained in 4 episodes

Sources

• On Evaluation of Embodied Navigation Agents
• Towards Deep Symbolic Reinforcement Learning
• Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
• Proximal Policy Optimization Algorithms

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