# Human-Allied Relational Reinforcement Learning

arXiv: https://arxiv.org/abs/2510.16188 · HTML: https://mlsanity.com/paper/2510.16188/

## Key facts

- Cited by 1 papers 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.

## Placed in

- Deep reinforcement learning methods / [Deep RL algorithms and representations](https://mlsanity.com/deeprl/deep-rl-algorithms-and-representations/)

## Builds on (most cited first, 20 of 20)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm](https://mlsanity.com/paper/1712.01815/) (2017)
- [StarCraft II: A New Challenge for Reinforcement Learning](https://mlsanity.com/paper/1708.04782/) (2017)
- [Reinforcement Learning Enhanced LLMs: A Survey](https://mlsanity.com/paper/2412.10400/) (2024)
- [Neural Logic Reinforcement Learning](https://mlsanity.com/paper/1904.10729/) (2019)
- [An Investigation into Pre-Training Object-Centric Representations for Reinforcement Learning](https://mlsanity.com/paper/2302.04419/) (2023)
- [Interpretable and Explainable Logical Policies via Neurally Guided Symbolic Abstraction](https://mlsanity.com/paper/2306.01439/) (2023)
- [OCAtari: Object-Centric Atari 2600 Reinforcement Learning Environments](https://mlsanity.com/paper/2306.08649/) (2023)
- [Entity-Centric Reinforcement Learning for Object Manipulation from Pixels](https://mlsanity.com/paper/2404.01220/) (2024)
- [Interpretable Concept Bottlenecks to Align Reinforcement Learning Agents](https://mlsanity.com/paper/2401.05821/) (2024)
- [Deep Explainable Relational Reinforcement Learning: A Neuro-Symbolic Approach](https://mlsanity.com/paper/2304.08349/) (2023)
- [BlendRL: A Framework for Merging Symbolic and Neural Policy Learning](https://mlsanity.com/paper/2410.11689/) (2024)
- [HackAtari: Atari Learning Environments for Robust and Continual Reinforcement Learning](https://mlsanity.com/paper/2406.03997/) (2024)
- [Explainable Action Advising for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2211.07882/) (2022)
- [Deep Reinforcement Learning Agents are not even close to Human Intelligence](https://mlsanity.com/paper/2505.21731/) (2025)
- [Fitted Q-Learning for Relational Domains](https://mlsanity.com/paper/2006.05595/) (2020)
- [Relational Neurosymbolic Markov Models](https://mlsanity.com/paper/2412.13023/) (2024)
- [Persistent Rule-based Interactive Reinforcement Learning](https://mlsanity.com/paper/2102.02441/) (2021)
- [Opinion-Guided Reinforcement Learning](https://mlsanity.com/paper/2405.17287/) (2024)

## Built on by (most cited first, 1 of 1)

- [GRAIL: Autonomous Concept Grounding for Neuro-Symbolic Reinforcement Learning](https://mlsanity.com/paper/2604.16871/) (2026)

## 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.