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PEORL: Integrating Symbolic Planning and Hierarchical Reinforcement Learning for Robust Decision-Making

arXiv 1804.07779 ↗First posted 20 Apr 2018cs.LG cs.AI stat.MLcited by 51

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Abstract (arXiv)

Reinforcement learning and symbolic planning have both been used to build intelligent autonomous agents. Reinforcement learning relies on learning from interactions with real world, which often requires an unfeasibly large amount of experience. Symbolic planning relies on manually crafted symbolic knowledge, which may not be robust to domain uncertainties and changes. In this paper we present a unified framework em PEORL that integrates symbolic planning with hierarchical reinforcement learning (HRL) to cope with decision-making in a dynamic environment with uncertainties. Symbolic plans are used to guide the agent's task execution and learning, and the learned experience is fed back to symbolic knowledge to improve planning. This method leads to rapid policy search and robust symbolic plans in complex domains. The framework is tested on benchmark domains of HRL.

Key facts. Cited by 51 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Apr 2018.

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Citing papers per year: 2021: 10; 2022: 14; 2023: 8; 2024: 6; 2025: 6; 2026: 7.

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  1. 2023Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysiscited by 99
  2. 2021A Survey on Interpretable Reinforcement Learningcited by 70
  3. 2022Learning Neuro-Symbolic Skills for Bilevel Planningcited by 55
  4. 2021Neuro-Symbolic Artificial Intelligence: Current Trendscited by 54
  5. 2021Learning Neuro-Symbolic Relational Transition Models for Bilevel Planningcited by 43
  6. 2021A Survey on Neural-symbolic Learning Systemscited by 39
  7. 2022Towards Data-and Knowledge-Driven Artificial Intelligence: A Survey on Neuro-Symbolic Computingcited by 34
  8. 2022LEAGUE: Guided Skill Learning and Abstraction for Long-Horizon Manipulationcited by 32
  9. 2022Reasoning with Scene Graphs for Robot Planning under Partial Observabilitycited by 25
  10. 2022Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill Diversitycited by 22
  11. 2022Intelligent problem-solving as integrated hierarchical reinforcement learningcited by 21
  12. 2023LOGICSEG: Parsing Visual Semantics with Neural Logic Learning and Reasoningcited by 17
38 more
  1. 2023Deep Explainable Relational Reinforcement Learning: A Neuro-Symbolic Approachcited by 15
  2. 2021Discovering User-Interpretable Capabilities of Black-Box Planning Agentscited by 10
  3. 2022Skill-based Multi-objective Reinforcement Learning of Industrial Robot Tasks with Planning and Knowledge Integrationcited by 10
  4. 2024Neuro-Symbolic AI: Explainability, Challenges, and Future Trendscited by 10
  5. 2025Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Surveycited by 10
  6. 2021Hierarchical Program-Triggered Reinforcement Learning Agents For Automated Drivingcited by 9
  7. 2022Online Learning of Reusable Abstract Models for Object Goal Navigationcited by 9
  8. 2025Tapas Are Free! Training-Free Adaptation of Programmatic Agents via LLM-Guided Program Synthesis in Dynamic Environmentscited by 9
  9. 2021Interpretable Model-based Hierarchical Reinforcement Learning using Inductive Logic Programmingcited by 8
  10. 2022Relative Behavioral Attributes: Filling the Gap between Symbolic Goal Specification and Reward Learning from Human Preferencescited by 7
  11. 2021Learning from Ambiguous Demonstrations with Self-Explanation Guided Reinforcement Learningcited by 5
  12. 2023Reinforcement Learning with Knowledge Representation and Reasoning: A Brief Surveycited by 5
  13. 2024Navigation in a simplified Urban Flow through Deep Reinforcement Learningcited by 5
  14. 2022Towards customizable reinforcement learning agents: Enabling preference specification through online vocabulary expansioncited by 4
  15. 2025Neuro-Symbolic Imitation Learning: Discovering Symbolic Abstractions for Skill Learningcited by 4
  16. 2026SCALAR: Learning and Composing Skills through LLM Guided Symbolic Planning and Deep RL Groundingcited by 4
  17. 2022Hybrid Deep Reinforcement Learning and Planning for Safe and Comfortable Automated Drivingcited by 3
  18. 2024EXPLORER: Exploration-guided Reasoning for Textual Reinforcement Learningcited by 3
  19. 2025Curiosity-Driven Imagination: Discovering Plan Operators and Learning Associated Policies for Open-World Adaptationcited by 3
  20. 2025SLAP: Shortcut Learning for Abstract Planningcited by 3
  21. 2022Hierarchical Reinforcement Learning with AI Planning Modelscited by 2
  22. 2023A Review of Symbolic, Subsymbolic and Hybrid Methods for Sequential Decision Makingcited by 2
  23. 2025Few-Shot Neuro-Symbolic Imitation Learning for Long-Horizon Planning and Actingcited by 2
  24. 2021TDM: Trustworthy Decision-Making via Interpretability Enhancementcited by 1
  25. 2021Creativity of AI: Hierarchical Planning Model Learning for Facilitating Deep Reinforcement Learningcited by 1
  26. 2023Hierarchical Task Network Planning for Facilitating Cooperative Multi-Agent Reinforcement Learningcited by 1
  27. 2023Program Machine Policy: Addressing Long-Horizon Tasks by Integrating Program Synthesis and State Machinescited by 1
  28. 2024Which Experiences Are Influential for RL Agents? Efficiently Estimating The Influence of Experiencescited by 1
  29. 2026The Price Is Not Right: Neuro-Symbolic Methods Outperform VLAs on Structured Long-Horizon Manipulation Tasks with Significantly Lower Energy Consumptioncited by 1
  30. 2026Novelty Adaptation Through Hybrid Large Language Model (LLM)-Symbolic Planning and LLM-guided Reinforcement Learningcited by 1
  31. 2026Towards Neuro-symbolic Causal Rule Synthesis, Verification, and Evaluation Grounded in Legal and Safety Principlescited by 1
  32. 2022SAGE: Generating Symbolic Goals for Myopic Models in Deep Reinforcement Learningcited by 0
  33. 2022Multi-Task Option Learning and Discovery for Stochastic Path Planningcited by 0
  34. 2023Which Experiences Are Influential for Your Agent? Policy Iteration with Turn-over Dropoutcited by 0
  35. 2024Maximally Permissive Reward Machinescited by 0
  36. 2024Bridging the Gap: Representation Spaces in Neuro-Symbolic AIcited by 0
  37. 2026Breaking Task Impasses Quickly: Adaptive Neuro-Symbolic Learning for Open-World Roboticscited by 0
  38. 2026Build on Priors: Vision--Language--Guided Neuro-Symbolic Imitation Learning for Data-Efficient Real-World Robot Manipulationcited by 0

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