{"total":56,"rows":[{"id":"2112.13112","title":"A Survey on Interpretable Reinforcement Learning","created":"2021-12-24","cited_by_count":73,"area":null},{"id":"2310.18308","title":"Gen2Sim: Scaling up Robot Learning in Simulation with Generative Models","created":"2023-10-27","cited_by_count":64,"area":null},{"id":"2206.10680","title":"Learning Neuro-Symbolic Skills for Bilevel Planning","created":"2022-06-21","cited_by_count":58,"area":null},{"id":"2105.05330","title":"Neuro-Symbolic Artificial Intelligence: Current Trends","created":"2021-05-11","cited_by_count":54,"area":null},{"id":"2306.01439","title":"Interpretable and Explainable Logical Policies via Neurally Guided Symbolic Abstraction","created":"2023-06-02","cited_by_count":46,"area":null},{"id":"2105.14074","title":"Learning Neuro-Symbolic Relational Transition Models for Bilevel Planning","created":"2021-05-28","cited_by_count":43,"area":null},{"id":"2111.08164","title":"A Survey on Neural-symbolic Learning Systems","created":"2021-11-10","cited_by_count":39,"area":null},{"id":"2210.15889","title":"Towards Data-and Knowledge-Driven Artificial Intelligence: A Survey on Neuro-Symbolic Computing","created":"2022-10-28","cited_by_count":35,"area":null},{"id":"2304.14391","title":"Energy-based Models are Zero-Shot Planners for Compositional Scene Rearrangement","created":"2023-04-27","cited_by_count":30,"area":null},{"id":"2102.12571","title":"The Logical Options Framework","created":"2021-02-24","cited_by_count":25,"area":null},{"id":"2311.09817","title":"Neural-Logic Human-Object Interaction Detection","created":"2023-11-16","cited_by_count":25,"area":null},{"id":"2309.01038","title":"Neurosymbolic Reinforcement Learning and Planning: A Survey","created":"2023-09-02","cited_by_count":24,"area":null},{"id":"2202.02886","title":"Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill Diversity","created":"2022-02-06","cited_by_count":22,"area":null},{"id":"2208.08731","title":"Intelligent problem-solving as integrated hierarchical reinforcement learning","created":"2022-08-18","cited_by_count":21,"area":null},{"id":"2303.04926","title":"Automated Cyber Defence: A Review","created":"2023-03-08","cited_by_count":21,"area":null},{"id":"2102.11529","title":"Differentiable Logic Machines","created":"2021-02-23","cited_by_count":18,"area":null},{"id":"2309.13556","title":"LOGICSEG: Parsing Visual Semantics with Neural Logic Learning and Reasoning","created":"2023-09-24","cited_by_count":17,"area":null},{"id":"2304.08349","title":"Deep Explainable Relational Reinforcement Learning: A Neuro-Symbolic Approach","created":"2023-04-17","cited_by_count":15,"area":null},{"id":"2206.12493","title":"RAPid-Learn: A Framework for Learning to Recover for Handling Novelties in Open-World Environments","created":"2022-06-24","cited_by_count":13,"area":null},{"id":"2109.11178","title":"Hierarchies of Planning and Reinforcement Learning for Robot Navigation","created":"2021-09-23","cited_by_count":11,"area":null},{"id":"2107.13668","title":"Discovering User-Interpretable Capabilities of Black-Box Planning Agents","created":"2021-07-28","cited_by_count":10,"area":null},{"id":"2411.04383","title":"Neuro-Symbolic AI: Explainability, Challenges, and Future Trends","created":"2024-11-07","cited_by_count":10,"area":null},{"id":"2103.08228","title":"Learning Symbolic Rules for Interpretable Deep Reinforcement Learning","created":"2021-03-15","cited_by_count":9,"area":null},{"id":"2212.14849","title":"Symbolic Visual Reinforcement Learning: A Scalable Framework with Object-Level Abstraction and Differentiable Expression Search","created":"2022-12-30","cited_by_count":9,"area":null},{"id":"2106.11417","title":"Interpretable Model-based Hierarchical Reinforcement Learning using Inductive Logic Programming","created":"2021-06-21","cited_by_count":8,"area":null},{"id":"2112.15319","title":"A Critical Review of Inductive Logic Programming Techniques for Explainable AI","created":"2021-12-31","cited_by_count":7,"area":null},{"id":"2105.07099","title":"Feature-Based Interpretable Reinforcement Learning based on State-Transition Models","created":"2021-05-14","cited_by_count":6,"area":null},{"id":"2207.01911","title":"Explainability in Deep Reinforcement Learning, a Review into Current Methods and Applications","created":"2022-07-05","cited_by_count":6,"area":null},{"id":"2309.07675","title":"Goal Space Abstraction in Hierarchical Reinforcement Learning via Set-Based Reachability Analysis","created":"2023-09-14","cited_by_count":6,"area":null},{"id":"2412.07207","title":"MAPLE: A Framework for Active Preference Learning Guided by Large Language Models","created":"2024-12-10","cited_by_count":6,"area":null},{"id":"2110.05286","title":"Learning from Ambiguous Demonstrations with Self-Explanation Guided Reinforcement Learning","created":"2021-10-11","cited_by_count":5,"area":null},{"id":"2304.12090","title":"Reinforcement Learning with Knowledge Representation and Reasoning: A Brief Survey","created":"2023-04-24","cited_by_count":5,"area":null},{"id":"2603.09036","title":"SCALAR: Learning and Composing Skills through LLM Guided Symbolic Planning and Deep RL Grounding","created":"2026-03-10","cited_by_count":5,"area":null},{"id":"2210.15096","title":"Towards customizable reinforcement learning agents: Enabling preference specification through online vocabulary expansion","created":"2022-10-27","cited_by_count":4,"area":null},{"id":"2112.10007","title":"Online Grounding of Symbolic Planning Domains in Unknown Environments","created":"2021-12-18","cited_by_count":3,"area":null},{"id":"2207.05018","title":"Learning Temporally Extended Skills in Continuous Domains as Symbolic Actions for Planning","created":"2022-07-11","cited_by_count":3,"area":null},{"id":"2211.06665","title":"A Survey on Explainable Reinforcement Learning: Concepts, Algorithms, Challenges","created":"2022-11-12","cited_by_count":3,"area":null},{"id":"2403.10692","title":"EXPLORER: Exploration-guided Reasoning for Textual Reinforcement Learning","created":"2024-03-15","cited_by_count":3,"area":null},{"id":"2103.15171","title":"A Bayesian Approach to Identifying Representational Errors","created":"2021-03-28","cited_by_count":2,"area":null},{"id":"2203.00669","title":"Hierarchical Reinforcement Learning with AI Planning Models","created":"2022-03-01","cited_by_count":2,"area":null},{"id":"2211.06733","title":"Deep Reinforcement Learning with Vector Quantized Encoding","created":"2022-11-12","cited_by_count":2,"area":null},{"id":"2304.10590","title":"A Review of Symbolic, Subsymbolic and Hybrid Methods for Sequential Decision Making","created":"2023-04-20","cited_by_count":2,"area":null},{"id":"2108.06080","title":"TDM: Trustworthy Decision-Making via Interpretability Enhancement","created":"2021-08-13","cited_by_count":1,"area":null},{"id":"2112.09836","title":"Creativity of AI: Hierarchical Planning Model Learning for Facilitating Deep Reinforcement Learning","created":"2021-12-18","cited_by_count":1,"area":null},{"id":"2201.08520","title":"Learning Two-Step Hybrid Policy for Graph-Based Interpretable Reinforcement Learning","created":"2022-01-21","cited_by_count":1,"area":null},{"id":"2206.13433","title":"Interpretable Hidden Markov Model-Based Deep Reinforcement Learning Hierarchical Framework for Predictive Maintenance of Turbofan Engines","created":"2022-06-27","cited_by_count":1,"area":null},{"id":"2305.00508","title":"Learning Achievement Structure for Structured Exploration in Domains with Sparse Reward","created":"2023-04-30","cited_by_count":1,"area":null},{"id":"2306.08359","title":"Hierarchical Task Network Planning for Facilitating Cooperative Multi-Agent Reinforcement Learning","created":"2023-06-14","cited_by_count":1,"area":null},{"id":"2310.15333","title":"Safe and Interpretable Estimation of Optimal Treatment Regimes","created":"2023-10-23","cited_by_count":1,"area":null},{"id":"2603.08814","title":"Scale-Plan: Scalable Language-Enabled Task Planning for Heterogeneous Multi-Robot Teams","created":"2026-03-09","cited_by_count":1,"area":null},{"id":"2609.36473","title":"Going Beyond State-Reaching: Learning Abstractions for Intrinsically Motivated Option Discovery","created":"2026-09-29","cited_by_count":1,"area":null},{"id":"2203.05079","title":"SAGE: Generating Symbolic Goals for Myopic Models in Deep Reinforcement Learning","created":"2022-03-09","cited_by_count":0,"area":null},{"id":"2210.00068","title":"Multi-Task Option Learning and Discovery for Stochastic Path Planning","created":"2022-09-30","cited_by_count":0,"area":null},{"id":"2301.11168","title":"Which Experiences Are Influential for Your Agent? Policy Iteration with Turn-over Dropout","created":"2023-01-26","cited_by_count":0,"area":null},{"id":"2408.08059","title":"Maximally Permissive Reward Machines","created":"2024-08-15","cited_by_count":0,"area":null},{"id":"2609.16056","title":"Managing Action Preconditions in Neuro-Symbolic RL: Three Placement Strategies for Embodied Agents","created":"2026-09-13","cited_by_count":0,"area":null}],"next_cursor":null}