# Dynamic planning in hierarchical active inference

arXiv: https://arxiv.org/abs/2402.11658 · HTML: https://mlsanity.com/paper/2402.11658/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 37 of its 138 references that are in the map.
- First posted on arXiv 18 Feb 2024.

## Builds on (most cited first, 30 of 37)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Active inference on discrete state-spaces: a synthesis](https://mlsanity.com/paper/2001.07203/) (2020)
- [Predictive Coding Approximates Backprop along Arbitrary Computation Graphs](https://mlsanity.com/paper/2006.04182/) (2020)
- [Deep active inference agents using Monte-Carlo methods](https://mlsanity.com/paper/2006.04176/) (2020)
- [The free energy principle for action and perception: A mathematical review](https://mlsanity.com/paper/1705.09156/) (2017)
- [Sophisticated Inference](https://mlsanity.com/paper/2006.04120/) (2020)
- [Deep Active Inference as Variational Policy Gradients](https://mlsanity.com/paper/1907.03876/) (2019)
- [Active Inference in Robotics and Artificial Agents: Survey and Challenges](https://mlsanity.com/paper/2112.01871/) (2021)
- [The Neural Coding Framework for Learning Generative Models](https://mlsanity.com/paper/2012.03405/) (2020)
- [World Models and Predictive Coding for Cognitive and Developmental Robotics: Frontiers and Challenges](https://mlsanity.com/paper/2301.05832/) (2023)
- [End-to-End Pixel-Based Deep Active Inference for Body Perception and Action](https://mlsanity.com/paper/2001.05847/) (2019)
- [Active Inference and Behavior Trees for Reactive Action Planning and Execution in Robotics](https://mlsanity.com/paper/2011.09756/) (2020)
- [Multimodal VAE Active Inference Controller](https://mlsanity.com/paper/2103.04412/) (2021)
- [On efficient computation in active inference](https://mlsanity.com/paper/2307.00504/) (2023)
- [Bayesian policy selection using active inference](https://mlsanity.com/paper/1904.08149/) (2019)
- [A Novel Adaptive Controller for Robot Manipulators based on Active Inference](https://mlsanity.com/paper/1909.12768/) (2019)
- [Robot self/other distinction: active inference meets neural networks learning in a mirror](https://mlsanity.com/paper/2004.05473/) (2020)
- [Active Predictive Coding: A Unified Neural Framework for Learning Hierarchical World Models for Perception and Planning](https://mlsanity.com/paper/2210.13461/) (2022)
- [Active Inference for Integrated State-Estimation, Control, and Learning](https://mlsanity.com/paper/2005.05894/) (2020)
- [Hierarchical generative modelling for autonomous robots](https://mlsanity.com/paper/2308.07775/) (2023)
- [A deep active inference model of the rubber-hand illusion](https://mlsanity.com/paper/2008.07408/) (2020)
- [Interactive inference: a multi-agent model of cooperative joint actions](https://mlsanity.com/paper/2210.13113/) (2022)
- [Spatial and Temporal Hierarchy for Autonomous Navigation using Active Inference in Minigrid Environment](https://mlsanity.com/paper/2312.05058/) (2023)
- [Free Energy Principle for State and Input Estimation of a Quadcopter Flying in Wind](https://mlsanity.com/paper/2109.12052/) (2021)
- [Adaptation through prediction: multisensory active inference torque control](https://mlsanity.com/paper/2112.06752/) (2021)
- [Disentangling Shape and Pose for Object-Centric Deep Active Inference Models](https://mlsanity.com/paper/2209.09097/) (2022)
- [Active Predicting Coding: Brain-Inspired Reinforcement Learning for Sparse Reward Robotic Control Problems](https://mlsanity.com/paper/2209.09174/) (2022)
- [Deconstructing deep active inference](https://mlsanity.com/paper/2303.01618/) (2023)
- [Symmetry and Complexity in Object-Centric Deep Active Inference Models](https://mlsanity.com/paper/2304.14493/) (2023)

## Abstract (arXiv)

By dynamic planning, we refer to the ability of the human brain to infer and impose motor trajectories related to cognitive decisions. A recent paradigm, active inference, brings fundamental insights into the adaptation of biological organisms, constantly striving to minimize prediction errors to restrict themselves to life-compatible states. Over the past years, many studies have shown how human and animal behaviors could be explained in terms of active inference - either as discrete decision-making or continuous motor control - inspiring innovative solutions in robotics and artificial intelligence. Still, the literature lacks a comprehensive outlook on effectively planning realistic actions in changing environments. Setting ourselves the goal of modeling complex tasks such as tool use, we delve into the topic of dynamic planning in active inference, keeping in mind two crucial aspects of biological behavior: the capacity to understand and exploit affordances for object manipulation, and to learn the hierarchical interactions between the self and the environment, including other agents. We start from a simple unit and gradually describe more advanced structures, comparing recently proposed design choices and providing basic examples. This study distances itself from traditional views centered on neural networks and reinforcement learning, and points toward a yet unexplored direction in active inference: hybrid representations in hierarchical models.