mlsanityData to 14 Oct 2026

SDRL: Interpretable and Data-efficient Deep Reinforcement Learning Leveraging Symbolic Planning

arXiv 1811.00090 ↗First posted 31 Oct 2018cs.AIcited by 78

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Abstract (arXiv)

Deep reinforcement learning (DRL) has gained great success by learning directly from high-dimensional sensory inputs, yet is notorious for the lack of interpretability. Interpretability of the subtasks is critical in hierarchical decision-making as it increases the transparency of black-box-style DRL approach and helps the RL practitioners to understand the high-level behavior of the system better. In this paper, we introduce symbolic planning into DRL and propose a framework of Symbolic Deep Reinforcement Learning (SDRL) that can handle both high-dimensional sensory inputs and symbolic planning. The task-level interpretability is enabled by relating symbolic actions to options.This framework features a planner -- controller -- meta-controller architecture, which takes charge of subtask scheduling, data-driven subtask learning, and subtask evaluation, respectively. The three components cross-fertilize each other and eventually converge to an optimal symbolic plan along with the learned subtasks, bringing together the advantages of long-term planning capability with symbolic knowledge and end-to-end reinforcement learning directly from a high-dimensional sensory input. Experimental results validate the interpretability of subtasks, along with improved data efficiency compared with state-of-the-art approaches.

Key facts. Cited by 78 papers in the map (data through 14 Oct 2026). First posted on arXiv 31 Oct 2018.

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Citing papers per year: 2021: 18; 2022: 16; 2023: 16; 2024: 15; 2025: 6; 2026: 7.

78 papers.

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  1. 2021A Survey on Interpretable Reinforcement Learningcited by 73
  2. 2023Gen2Sim: Scaling up Robot Learning in Simulation with Generative Modelscited by 64
  3. 2022Learning Neuro-Symbolic Skills for Bilevel Planningcited by 58
  4. 2021Neuro-Symbolic Artificial Intelligence: Current Trendscited by 54
  5. 2023Interpretable and Explainable Logical Policies via Neurally Guided Symbolic Abstractioncited by 46
  6. 2021Learning Neuro-Symbolic Relational Transition Models for Bilevel Planningcited by 43
  7. 2021A Survey on Neural-symbolic Learning Systemscited by 39
  8. 2022Towards Data-and Knowledge-Driven Artificial Intelligence: A Survey on Neuro-Symbolic Computingcited by 35
  9. 2023Energy-based Models are Zero-Shot Planners for Compositional Scene Rearrangementcited by 30
  10. 2021The Logical Options Frameworkcited by 25
  11. 2023Neural-Logic Human-Object Interaction Detectioncited by 25
  12. 2023Neurosymbolic Reinforcement Learning and Planning: A Surveycited by 24
38 more
  1. 2022Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill Diversitycited by 22
  2. 2022Intelligent problem-solving as integrated hierarchical reinforcement learningcited by 21
  3. 2023Automated Cyber Defence: A Reviewcited by 21
  4. 2021Differentiable Logic Machinescited by 18
  5. 2023LOGICSEG: Parsing Visual Semantics with Neural Logic Learning and Reasoningcited by 17
  6. 2024End-to-End Neuro-Symbolic Reinforcement Learning with Textual Explanationscited by 17
  7. 2023Deep Explainable Relational Reinforcement Learning: A Neuro-Symbolic Approachcited by 15
  8. 2024BlendRL: A Framework for Merging Symbolic and Neural Policy Learningcited by 14
  9. 2022RAPid-Learn: A Framework for Learning to Recover for Handling Novelties in Open-World Environmentscited by 13
  10. 2021Hierarchies of Planning and Reinforcement Learning for Robot Navigationcited by 11
  11. 2024SPIRE: Synergistic Planning, Imitation, and Reinforcement Learning for Long-Horizon Manipulationcited by 11
  12. 2021Discovering User-Interpretable Capabilities of Black-Box Planning Agentscited by 10
  13. 2024Neuro-Symbolic AI: Explainability, Challenges, and Future Trendscited by 10
  14. 2021Learning Symbolic Rules for Interpretable Deep Reinforcement Learningcited by 9
  15. 2021Hierarchical Program-Triggered Reinforcement Learning Agents For Automated Drivingcited by 9
  16. 2022Symbolic Visual Reinforcement Learning: A Scalable Framework with Object-Level Abstraction and Differentiable Expression Searchcited by 9
  17. 2025Tapas Are Free! Training-Free Adaptation of Programmatic Agents via LLM-Guided Program Synthesis in Dynamic Environmentscited by 9
  18. 2021Interpretable Model-based Hierarchical Reinforcement Learning using Inductive Logic Programmingcited by 8
  19. 2021A Critical Review of Inductive Logic Programming Techniques for Explainable AIcited by 7
  20. 2021Feature-Based Interpretable Reinforcement Learning based on State-Transition Modelscited by 6
  21. 2022Explainability in Deep Reinforcement Learning, a Review into Current Methods and Applicationscited by 6
  22. 2023Goal Space Abstraction in Hierarchical Reinforcement Learning via Set-Based Reachability Analysiscited by 6
  23. 2024Reconciling Spatial and Temporal Abstractions for Goal Representationcited by 6
  24. 2024MAPLE: A Framework for Active Preference Learning Guided by Large Language Modelscited by 6
  25. 2021Learning from Ambiguous Demonstrations with Self-Explanation Guided Reinforcement Learningcited by 5
  26. 2023Reinforcement Learning with Knowledge Representation and Reasoning: A Brief Surveycited by 5
  27. 2024EXPIL: Explanatory Predicate Invention for Learning in Gamescited by 5
  28. 2026SCALAR: Learning and Composing Skills through LLM Guided Symbolic Planning and Deep RL Groundingcited by 5
  29. 2022Towards customizable reinforcement learning agents: Enabling preference specification through online vocabulary expansioncited by 4
  30. 2025Neuro-Symbolic Imitation Learning: Discovering Symbolic Abstractions for Skill Learningcited by 4
  31. 2026MINT: Minimal Information Neuro-Symbolic Tree for Objective-Driven Knowledge-Gap Reasoning and Active Elicitationcited by 4
  32. 2021Online Grounding of Symbolic Planning Domains in Unknown Environmentscited by 3
  33. 2022Learning Temporally Extended Skills in Continuous Domains as Symbolic Actions for Planningcited by 3
  34. 2022A Survey on Explainable Reinforcement Learning: Concepts, Algorithms, Challengescited by 3
  35. 2024EXPLORER: Exploration-guided Reasoning for Textual Reinforcement Learningcited by 3
  36. 2024SkillTree: Explainable Skill-Based Deep Reinforcement Learning for Long-Horizon Control Taskscited by 3
  37. 2021A Bayesian Approach to Identifying Representational Errorscited by 2
  38. 2022Hierarchical Reinforcement Learning with AI Planning Modelscited by 2

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