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[2503.13194] A representational framework for learning and encoding structurally enriched trajectories in complex agent environments
Summary
The paper presents a framework for learning and encoding structurally enriched trajectories in complex environments, enhancing AI agents' decision-making capabilities.
Why It Matters
This research addresses the limitations of traditional AI representations in complex scenarios, proposing a novel approach that improves generalization and performance in tasks with sparse rewards. By integrating hierarchical relations and multi-level graphs, it enhances the understanding of agent dynamics, which is crucial for advancing AI applications in real-world environments.
Key Takeaways
- Introduces Structurally Enriched Trajectories (SETs) for better representation of agent actions.
- Proposes a new architecture, SETLE, that utilizes heterogeneous graph-based memory structures.
- Demonstrates measurable improvements in task performance, particularly in complex environments.
- Enhances the ability of AI agents to recognize structural patterns across various tasks.
- Integrates with reinforcement learning to achieve breakthrough success rates.
Computer Science > Artificial Intelligence arXiv:2503.13194 (cs) [Submitted on 17 Mar 2025 (v1), last revised 16 Feb 2026 (this version, v3)] Title:A representational framework for learning and encoding structurally enriched trajectories in complex agent environments Authors:Corina Catarau-Cotutiu, Esther Mondragon, Eduardo Alonso View a PDF of the paper titled A representational framework for learning and encoding structurally enriched trajectories in complex agent environments, by Corina Catarau-Cotutiu and 2 other authors View PDF HTML (experimental) Abstract:The ability of artificial intelligence agents to make optimal decisions and generalise them to different domains and tasks is compromised in complex scenarios. One way to address this issue has focused on learning efficient representations of the world and on how the actions of agents affect them in state-action transitions. Whereas such representations are procedurally efficient, they lack structural richness. To address this problem, we propose to enhance the agent's ontology and extend the traditional conceptualisation of trajectories to provide a more nuanced view of task execution. Structurally Enriched Trajectories (SETs) extend the encoding of sequences of states and their transitions by incorporating hierarchical relations between objects, interactions, and affordances. SETs are built as multi-level graphs, providing a detailed representation of the agent dynamics and a transferable functional abstraction o...
