mlsanityData to 14 Oct 2026

Human-Allied Relational Reinforcement Learning

arXiv 2510.16188 ↗First posted 17 Oct 2025cs.LGcited by 1

On the map: Deep reinforcement learning methods → Deep RL algorithms and representations

Abstract (arXiv)

Reinforcement learning (RL) has experienced a second wind in the past decade. While incredibly successful in images and videos, these systems still operate within the realm of propositional tasks ignoring the inherent structure that exists in the problem. Consequently, relational extensions (RRL) have been developed for such structured problems that allow for effective generalization to arbitrary number of objects. However, they inherently make strong assumptions about the problem structure. We introduce a novel framework that combines RRL with object-centric representation to handle both structured and unstructured data. We enhance learning by allowing the system to actively query the human expert for guidance by explicitly modeling the uncertainty over the policy. Our empirical evaluation demonstrates the effectiveness and efficiency of our proposed approach.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods. Builds on 20 of its 48 references that are in the map. First posted on arXiv 17 Oct 2025.

Builds on

20 of 48 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024HackAtari: Atari Learning Environments for Robust and Continual Reinforcement Learning6 Jun

Other references · 19

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  3. 2017Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithmcited by 734
  4. 2017StarCraft II: A New Challenge for Reinforcement Learningcited by 274
  5. 2024Reinforcement Learning Enhanced LLMs: A Surveycited by 71
  6. 2019Neural Logic Reinforcement Learningcited by 53
  7. 2023An Investigation into Pre-Training Object-Centric Representations for Reinforcement Learningcited by 49
  8. 2023Interpretable and Explainable Logical Policies via Neurally Guided Symbolic Abstractioncited by 46
  9. 2023OCAtari: Object-Centric Atari 2600 Reinforcement Learning Environmentscited by 30
  10. 2024Entity-Centric Reinforcement Learning for Object Manipulation from Pixelscited by 27
  11. 2024Interpretable Concept Bottlenecks to Align Reinforcement Learning Agentscited by 23
  12. 2023Deep Explainable Relational Reinforcement Learning: A Neuro-Symbolic Approachcited by 15
7 more
  1. 2024BlendRL: A Framework for Merging Symbolic and Neural Policy Learningcited by 14
  2. 2022Explainable Action Advising for Multi-Agent Reinforcement Learningcited by 10
  3. 2025Deep Reinforcement Learning Agents are not even close to Human Intelligencecited by 8
  4. 2020Fitted Q-Learning for Relational Domainscited by 7
  5. 2024Relational Neurosymbolic Markov Modelscited by 6
  6. 2021Persistent Rule-based Interactive Reinforcement Learningcited by 5
  7. 2024Opinion-Guided Reinforcement Learningcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026GRAIL: Autonomous Concept Grounding for Neuro-Symbolic Reinforcement Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2022Learning Rules from Rewards
  2. 2023Relational Object-Centric Actor-Critic
  3. 2023Learning Dynamic Attribute-factored World Models for Efficient Multi-object Reinforcement Learning
  4. 2021Improving Reinforcement Learning with Human Assistance: An Argument for Human Subject Studies with HIPPO Gym
  5. 2026Answer-Set-Programming-based Abstractions for Reinforcement Learning
  6. 2025Combining Planning and Reinforcement Learning for Solving Relational Multiagent Domains
  7. 2024Investigating Relational State Abstraction in Collaborative MARL
  8. 2024A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback