Data to 14 Oct 2026

Neural Logic Reinforcement Learning

arXiv 1904.10729 ↗First posted 24 Apr 2019cs.LGcited by 53

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Abstract (arXiv)

Deep reinforcement learning (DRL) has achieved significant breakthroughs in various tasks. However, most DRL algorithms suffer a problem of generalizing the learned policy which makes the learning performance largely affected even by minor modifications of the training environment. Except that, the use of deep neural networks makes the learned policies hard to be interpretable. To address these two challenges, we propose a novel algorithm named Neural Logic Reinforcement Learning (NLRL) to represent the policies in reinforcement learning by first-order logic. NLRL is based on policy gradient methods and differentiable inductive logic programming that have demonstrated significant advantages in terms of interpretability and generalisability in supervised tasks. Extensive experiments conducted on cliff-walking and blocks manipulation tasks demonstrate that NLRL can induce interpretable policies achieving near-optimal performance while demonstrating good generalisability to environments of different initial states and problem sizes.

Key facts. Cited by 53 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Apr 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 9; 2022: 9; 2023: 8; 2024: 12; 2025: 11; 2026: 4.

53 papers.

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2021A Survey on Interpretable Reinforcement Learningcited by 73
2021Neuro-Symbolic Artificial Intelligence: Current Trendscited by 54
2023Interpretable and Explainable Logical Policies via Neurally Guided Symbolic Abstractioncited by 46
2022Towards Data-and Knowledge-Driven Artificial Intelligence: A Survey on Neuro-Symbolic Computingcited by 35
2023OCAtari: Object-Centric Atari 2600 Reinforcement Learning Environmentscited by 30
2021Neuro-Symbolic Hierarchical Rule Inductioncited by 23
2024Interpretable Concept Bottlenecks to Align Reinforcement Learning Agentscited by 23
2024LLMs for Relational Reasoning: How Far are We?cited by 21
2021Differentiable Logic Machinescited by 18
2021Neuro-Symbolic Forward Reasoningcited by 16
2022GALOIS: Boosting Deep Reinforcement Learning via Generalizable Logic Synthesiscited by 16
2022Multi-Step Deductive Reasoning Over Natural Language: An Empirical Study on Out-of-Distribution Generalisationcited by 15

38 more

2023Deep Explainable Relational Reinforcement Learning: A Neuro-Symbolic Approachcited by 15
2024BlendRL: A Framework for Merging Symbolic and Neural Policy Learningcited by 14
2024Synthesizing Programmatic Reinforcement Learning Policies with Large Language Model Guided Searchcited by 13
2024PUZZLES: A Benchmark for Neural Algorithmic Reasoningcited by 13
2022MIXRTs: Toward Interpretable Multi-Agent Reinforcement Learning via Mixing Recurrent Soft Decision Treescited by 11
2022Explainable Reinforcement Learning via Model Transformscited by 11
2021Learning Symbolic Rules for Interpretable Deep Reinforcement Learningcited by 9
2021Interpretable Model-based Hierarchical Reinforcement Learning using Inductive Logic Programmingcited by 8
2023What Planning Problems Can A Relational Neural Network Solve?cited by 8
2025Multimodal LLM-assisted Evolutionary Search for Programmatic Control Policiescited by 8
2021LOA: Logical Optimal Actions for Text-based Interaction Gamescited by 6
2023Reinforcement Learning with Knowledge Representation and Reasoning: A Brief Surveycited by 5
2024Understanding What Affects the Generalization Gap in Visual Reinforcement Learning: Theory and Empirical Evidencecited by 5
2024EXPIL: Explanatory Predicate Invention for Learning in Gamescited by 5
2024Mitigating Information Loss in Tree-Based Reinforcement Learning via Direct Optimizationcited by 5
2022Neural Meta-Symbolic Reasoning and Learningcited by 4
2023Learning Symbolic Rules over Abstract Meaning Representations for Textual Reinforcement Learningcited by 4
2024Interpretable end-to-end Neurosymbolic Reinforcement Learning agentscited by 4
2025Neural DNF-MT: A Neuro-symbolic Approach for Learning Interpretable and Editable Policiescited by 4
2025Rule-Guided Reinforcement Learning Policy Evaluation and Improvementcited by 4
2025Reinforcement Learning with Physics-Informed Symbolic Program Priors for Zero-Shot Wireless Indoor Navigationcited by 4
2022A Survey on Explainable Reinforcement Learning: Concepts, Algorithms, Challengescited by 3
2023Learning Reliable Logical Rules with SATNetcited by 3
2024SMOSE: Sparse Mixture of Shallow Experts for Interpretable Reinforcement Learning in Continuous Control Taskscited by 3
2025Learning from Less: Guiding Deep Reinforcement Learning with Differentiable Symbolic Planningcited by 3
2021Grid-to-Graph: Flexible Spatial Relational Inductive Biases for Reinforcement Learningcited by 2
2024XRL-Bench: A Benchmark for Evaluating and Comparing Explainable Reinforcement Learning Techniquescited by 2
2023Deep Inductive Logic Programming meets Reinforcement Learningcited by 1
2025Inducing, Detecting and Characterising Neural Modules: A Pipeline for Functional Interpretability in Reinforcement Learningcited by 1
2025Towards Automated Semantic Interpretability in Reinforcement Learning via Vision-Language Modelscited by 1
2025Human-Allied Relational Reinforcement Learningcited by 1
2022Neural NID Rulescited by 0
2022Learning Rules from Rewardscited by 0
2024LLM-Augmented Symbolic Reinforcement Learning with Landmark-Based Task Decompositioncited by 0
2025SALSA-RL: Stability Analysis in the Latent Space of Actions for Reinforcement Learningcited by 0
2025Mission-Aligned Learning-Informed Control of Autonomous Systems: Formulation and Foundationscited by 0
2025Hierarchical Reinforcement Learning with Targeted Causal Interventionscited by 0
2026Boosting deep Reinforcement Learning using pretraining with Logical Optionscited by 0

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