Hierarchical Active Inference using Successor Representations

2026-04-21 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

A new model for hierarchical active inference, combining a hierarchical environmental model with successor representations, addresses the challenge of scaling active inference to complex, large-scale problems. This approach, inspired by multi-scale hierarchical representations in the brain, demonstrates how lower-level successor representations can learn higher-level abstract states and actions, facilitating efficient planning. The model's performance was evaluated across several planning and reinforcement learning tasks, including the four rooms task, a key-based navigation task, a partially observable planning problem, the Mountain Car problem, and PointMaze navigation tasks with continuous state and action spaces. Results indicate that this hierarchical method significantly outperforms flat active inference and Q-learning baselines in terms of training efficiency, planning steps, and stability, particularly in environments requiring long-horizon planning or dynamic goal re-planning. This represents the first application of learned hierarchical state and action abstractions to active inference within Free Energy Principle-based theories of brain function.

Key takeaway

For AI scientists and machine learning engineers developing agents for complex, large-scale environments, consider implementing hierarchical active inference with successor representations. This framework offers superior planning efficiency, faster learning convergence, and enhanced stability compared to traditional flat active inference or Q-learning, especially for tasks requiring long-horizon planning or rapid re-planning to new goals. Your agents will navigate complex, partially observable environments more effectively and robustly.

Key insights

Hierarchical active inference with successor representations enables efficient, scalable planning in complex, dynamic environments.

Principles

• Minimize variational free energy to reduce sensory surprise.
• Factor environment dynamics from goals using successor representations.
• Balance exploration and exploitation via expected free energy.

Method

The method clusters successor representations to form macro states, then learns macro actions between them using lower-level active inference. Higher-level active inference plans with these abstractions, significantly reducing computational cost.

In practice

• Discretize continuous state spaces for successor representation clustering.
• Use spectral clustering for robust macro state discovery.
• Employ RBF kernels as regularizers for smoother clustering in continuous spaces.

Topics

• Active Inference
• Successor Representations
• Hierarchical Planning
• Free Energy Principle
• Spectral Clustering

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

Related on AIssential

• Hierarchical Active Inference using Successor Representations — Hierarchical active inference with successor representations enables scalable planning by learning abstract states and actions.
• Why Learn if You Can Infer: Active Inference for Robot Planning & Control — Active inference and object-centric priors enable rapid, human-level AI performance in complex game environments.
• Efficient Exploration, Reasoning, and Training-Free MTP — Efficiency gains in LLMs are achievable through online RLHF, balanced reasoning control, and embedding-space multi-token prediction.
• Representation over Routing: Overcoming Surrogate Hacking in Multi-Timescale PPO — Blindly fusing multi-timescale signals in PPO can cause surrogate hacking or myopic degeneration.
• Abstraction for Offline Goal-Conditioned Reinforcement Learning — Hierarchy in offline GCRL enables absolute abstraction and experience reuse through relativised options.
• Playing Games With TheoryCoder | ARC Prize @ MIT — Theory-based reinforcement learning with hierarchical abstraction offers scalable, human-like efficiency for complex sequential decision problems.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.