Hierarchical Active Inference using Successor Representations

2026-04-17 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

A new model for hierarchical active inference combines a hierarchical environmental model with successor representations to address the challenge of scaling active inference to complex, large-scale problems. This approach, inspired by multi-scale brain representations, demonstrates how lower-level successor representations can learn higher-level abstract states and how lower-level active inference planning can bootstrap higher-level abstract actions. These learned abstractions facilitate efficient planning, as shown across several planning and reinforcement learning tasks. These tasks include a four rooms variant, a key-based navigation task, a partially observable planning problem, the Mountain Car problem, and the PointMaze family of continuous navigation tasks. This work represents the first application of learned hierarchical state and action abstractions to active inference within Free Energy Principle (FEP)-based theories.

Key takeaway

For AI Scientists developing planning and reinforcement learning systems, this research suggests a viable path to scaling active inference. You should consider integrating hierarchical models with successor representations to manage complexity and improve planning efficiency in large-scale, real-world environments, especially for tasks requiring multi-level abstraction.

Key insights

Hierarchical active inference with successor representations enables scalable planning by learning abstract states and actions.

Principles

• Multi-scale brain representations inspire hierarchical planning.
• Lower-level learning can bootstrap higher-level abstractions.

Method

The approach combines a hierarchical environmental model with successor representations, using active inference at lower levels to learn abstract states and actions for efficient planning.

In practice

• Apply to complex navigation tasks.
• Use for partially observable planning problems.

Topics

• Active Inference
• Successor Representations
• Hierarchical Planning
• Free Energy Principle
• Reinforcement Learning

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.