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Inter-Agent Relative Representations for Multi-Agent Option Discovery

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Option DiscoveryMulti-agent Reinforcement Learning
Abstract:

Temporally extended actions improve the ability to explore and plan in single-agent settings. In multi-agent settings, the exponential growth of the joint state space with the number of agents makes coordinated behaviours even more valuable. Yet, this same exponential growth renders the design of multi-agent options particularly challenging. Existing multi-agent option discovery methods often sacrifice coordination by producing loosely coupled or fully independent behaviors. Toward addressing these limitations, we describe a novel approach for multi-agent option discovery. Specifically, we propose a joint-state abstraction that compresses the state space while preserving the information necessary to discover strongly coordinated behaviours. Our approach builds on the inductive bias that synchronisation over agent states provides a natural foundation for coordination in the absence of explicit objectives. We first approximate a fictitious state of maximal alignment with the team, the Fermat state, and use it to define a measure of spreadness, capturing team-level misalignment on each individual state dimension. Building on this representation, we then employ a neural graph Laplacian estimator to derive options that capture state synchronisation patterns between agents. We evaluate the resulting options across multiple scenarios in two multi-agent domains, showing that they yield stronger downstream coordination capabilities compared to alternative option discovery methods.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper proposes a synchronization-based joint-state abstraction for multi-agent option discovery, using a Fermat state representation to measure team-level misalignment and guide coordinated behavior learning. It resides in the 'Synchronization-Based Joint-State Abstraction' leaf, which contains only two papers including this one. This sparse population suggests the specific approach of using geometric alignment measures for option discovery is relatively unexplored, though the broader hierarchical multi-agent option discovery branch addresses related coordination challenges through alternative mechanisms.

The taxonomy reveals three main research directions: hierarchical option discovery, explainability frameworks, and trajectory prediction. The paper's leaf sits within the hierarchical branch, adjacent to goal-conditioned high-level model approximation methods that use subgoal transitions rather than synchronization patterns. The explainability branch (mask-based collaboration analysis) and trajectory prediction branch (attention-based forecasting) address complementary aspects of multi-agent interaction but diverge in their core objectives—interpretability and spatial forecasting versus temporal abstraction for coordination. The paper's focus on relative state representations bridges geometric encoding ideas from trajectory prediction with hierarchical policy learning.

Among the three contributions analyzed, the Fermat n-distance abstraction examined ten candidates with none clearly refuting it, suggesting novelty in the geometric alignment formulation. The multi-agent option discovery method examined four candidates, also without refutation. However, the MacDec-POMDP framework extension examined ten candidates and found three potentially overlapping prior works, indicating this component may build more directly on established foundations. The analysis covered twenty-four total candidates from semantic search, providing a focused but not exhaustive view of the literature landscape.

The limited search scope (twenty-four candidates) and sparse taxonomy leaf (two papers) suggest the synchronization-based abstraction approach occupies a relatively novel position within multi-agent option discovery. However, the MacDec-POMDP extension shows clearer connections to existing frameworks, and the broader hierarchical reinforcement learning literature may contain additional relevant work not captured in this focused search. The novelty appears strongest in the geometric alignment formulation rather than the overall hierarchical coordination framework.

Taxonomy

Core-task Taxonomy Papers
4
3
Claimed Contributions
24
Contribution Candidate Papers Compared
3
Refutable Paper

Research Landscape Overview

Core task: Multi-agent option discovery using inter-agent relative state representations. The field addresses how groups of agents can learn reusable, temporally extended behaviors (options) that exploit relational structure among teammates. The taxonomy reveals three main branches. Hierarchical Multi-Agent Option Discovery focuses on methods that build temporal abstractions for coordinated action, often by identifying synchronization patterns or joint-state abstractions that capture when agents should act together. Multi-Agent Explainability and Collaboration Analysis emphasizes interpretability and understanding of team dynamics, examining how agents' decisions can be made transparent and how collaboration emerges. Relative Pose Encoding for Multi-Agent Trajectory Prediction tackles the geometric side of multi-agent interaction, using relative spatial encodings to forecast future trajectories in settings like autonomous driving. Together, these branches span the spectrum from learning coordinated policies to explaining agent behavior and predicting spatial motion. Within Hierarchical Multi-Agent Option Discovery, a particularly active line of work explores synchronization-based joint-state abstraction, where the goal is to identify when agents should coordinate their low-level actions under a shared high-level plan. Inter-Agent Relative Representations[0] sits squarely in this cluster, proposing to discover options by leveraging relative state encodings that capture inter-agent dependencies. It shares thematic ground with Coordinated Joint Options[4], which also emphasizes joint temporal abstractions, though the two may differ in how they represent or learn the relational structure. Nearby efforts like MAGIC-MASK[3] and Hierarchical Model Approximation[2] tackle related challenges of scalable abstraction and interpretability in multi-agent settings, highlighting ongoing questions about how to balance expressiveness, sample efficiency, and the ability to generalize across team compositions. The central trade-off remains whether to impose strong structural priors on coordination or to let data-driven methods discover emergent patterns.

Claimed Contributions

Inter-agent relative state abstraction via Fermat n-distances

The authors introduce a novel state representation that transforms the joint state space into an inter-agent relative representation centered around the Fermat state (the state of maximal alignment). This abstraction uses multi-dimensional n-distances to measure team-level misalignment across individual state dimensions, compressing the exponentially growing joint state space while preserving coordination-relevant information.

10 retrieved papers
Multi-agent option discovery method using relative representations

The authors propose a method for discovering joint options by performing graph Laplacian eigen-decomposition on the inter-agent relative state representations rather than raw joint states. This approach yields options that express strongly coordinated behaviours focused on inter-agent relational dynamics and state synchronisation patterns.

4 retrieved papers
Extension of MacDec-POMDP framework for joint options

The authors adapt the MacDec-POMDP framework to support multi-agent macro-actions (joint options) rather than only single-agent options. This includes defining joint options with team-level initiation sets and termination conditions, and introducing mechanisms for information sharing and synchronisation to ensure correct execution of collective behaviours.

10 retrieved papers
Can Refute

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Inter-agent relative state abstraction via Fermat n-distances

The authors introduce a novel state representation that transforms the joint state space into an inter-agent relative representation centered around the Fermat state (the state of maximal alignment). This abstraction uses multi-dimensional n-distances to measure team-level misalignment across individual state dimensions, compressing the exponentially growing joint state space while preserving coordination-relevant information.

Contribution

Multi-agent option discovery method using relative representations

The authors propose a method for discovering joint options by performing graph Laplacian eigen-decomposition on the inter-agent relative state representations rather than raw joint states. This approach yields options that express strongly coordinated behaviours focused on inter-agent relational dynamics and state synchronisation patterns.

Contribution

Extension of MacDec-POMDP framework for joint options

The authors adapt the MacDec-POMDP framework to support multi-agent macro-actions (joint options) rather than only single-agent options. This includes defining joint options with team-level initiation sets and termination conditions, and introducing mechanisms for information sharing and synchronisation to ensure correct execution of collective behaviours.

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