# Exploring and Learning Structure: Active Inference Approach in Navigational Agents

arXiv: https://arxiv.org/abs/2408.05982 · HTML: https://mlsanity.com/paper/2408.05982/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 32 references that are in the map.
- First posted on arXiv 12 Aug 2024.

## Builds on (most cited first, 9 of 9)

- [The StreetLearn Environment and Dataset](https://mlsanity.com/paper/1903.01292/) (2019)
- [Modeling Perceptual Aliasing in SLAM via Discrete-Continuous Graphical Models](https://mlsanity.com/paper/1810.11692/) (2018)
- [Bayesian model reduction](https://mlsanity.com/paper/1805.07092/) (2018)
- [Demystification of Few-shot and One-shot Learning](https://mlsanity.com/paper/2104.12174/) (2021)
- [Graph schemas as abstractions for transfer learning, inference, and planning](https://mlsanity.com/paper/2302.07350/) (2023)
- [Spatial and Temporal Hierarchy for Autonomous Navigation using Active Inference in Minigrid Environment](https://mlsanity.com/paper/2312.05058/) (2023)
- [Space is a latent sequence: Structured sequence learning as a unified theory of representation in the hippocampus](https://mlsanity.com/paper/2212.01508/) (2022)
- [Learning Cognitive Maps from Transformer Representations for Efficient Planning in Partially Observed Environments](https://mlsanity.com/paper/2401.05946/) (2024)
- [Bridging Cognitive Maps: a Hierarchical Active Inference Model of Spatial Alternation Tasks and the Hippocampal-Prefrontal Circuit](https://mlsanity.com/paper/2308.11463/) (2023)

## Built on by (most cited first, 5 of 5)

- [Possible Principles for Aligned Structure Learning Agents](https://mlsanity.com/paper/2410.00258/) (2024)
- [Free Energy Projective Simulation (FEPS): Active inference with interpretability](https://mlsanity.com/paper/2411.14991/) (2024)
- [Toward Universal and Interpretable World Models for Open-ended Learning Agents](https://mlsanity.com/paper/2409.18676/) (2024)
- [Deep Active Inference with Diffusion Policy and Multiple Timescale World Model for Real-World Exploration and Navigation](https://mlsanity.com/paper/2510.23258/) (2025)
- [Expected Free Energy-based Informative Path Planning for Robotic Mars Exploration](https://mlsanity.com/paper/2608.14466/) (2026)

## Abstract (arXiv)

Drawing inspiration from animal navigation strategies, we introduce a novel computational model for navigation and mapping, rooted in biologically inspired principles. Animals exhibit remarkable navigation abilities by efficiently using memory, imagination, and strategic decision-making to navigate complex and aliased environments. Building on these insights, we integrate traditional cognitive mapping approaches with an Active Inference Framework (AIF) to learn an environment structure in a few steps. Through the incorporation of topological mapping for long-term memory and AIF for navigation planning and structure learning, our model can dynamically apprehend environmental structures and expand its internal map with predicted beliefs during exploration. Comparative experiments with the Clone-Structured Graph (CSCG) model highlight our model's ability to rapidly learn environmental structures in a single episode, with minimal navigation overlap. this is achieved without prior knowledge of the dimensions of the environment or the type of observations, showcasing its robustness and effectiveness in navigating ambiguous environments.