# Learning Perception and Planning with Deep Active Inference

arXiv: https://arxiv.org/abs/2001.11841 · HTML: https://mlsanity.com/paper/2001.11841/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2020.

## Built on by (most cited first, 19 of 19)

- [pymdp: A Python library for active inference in discrete state spaces](https://mlsanity.com/paper/2201.03904/) (2022)
- [The Free Energy Principle for Perception and Action: A Deep Learning Perspective](https://mlsanity.com/paper/2207.06415/) (2022)
- [Designing Ecosystems of Intelligence from First Principles](https://mlsanity.com/paper/2212.01354/) (2022)
- [Contrastive Active Inference](https://mlsanity.com/paper/2110.10083/) (2021)
- [Branching Time Active Inference: the theory and its generality](https://mlsanity.com/paper/2111.11107/) (2021)
- [On efficient computation in active inference](https://mlsanity.com/paper/2307.00504/) (2023)
- [Exploration and preference satisfaction trade-off in reward-free learning](https://mlsanity.com/paper/2106.04316/) (2021)
- [Branching Time Active Inference: empirical study and complexity class analysis](https://mlsanity.com/paper/2111.11276/) (2021)
- [Prior Preference Learning from Experts:Designing a Reward with Active Inference](https://mlsanity.com/paper/2101.08937/) (2021)
- [LatentSLAM: unsupervised multi-sensor representation learning for localization and mapping](https://mlsanity.com/paper/2105.03265/) (2021)
- [Learning Policies for Continuous Control via Transition Models](https://mlsanity.com/paper/2209.08033/) (2022)
- [Branching Time Active Inference with Bayesian Filtering](https://mlsanity.com/paper/2112.07406/) (2021)
- [Multi-Modal and Multi-Factor Branching Time Active Inference](https://mlsanity.com/paper/2206.12503/) (2022)
- [Sophisticated Learning: A novel algorithm for active learning during model-based planning](https://mlsanity.com/paper/2308.08029/) (2023)
- [Free Energy Projective Simulation (FEPS): Active inference with interpretability](https://mlsanity.com/paper/2411.14991/) (2024)
- [The Missing Reward: Active Inference in the Era of Experience](https://mlsanity.com/paper/2508.05619/) (2025)
- [Differentiable Generalised Predictive Coding](https://mlsanity.com/paper/2112.03378/) (2021)
- [Environment-Centric Active Inference](https://mlsanity.com/paper/2408.12777/) (2024)
- [A memory-based active inference model of DishBrain-like adaptive behaviour](https://mlsanity.com/paper/2508.06980/) (2025)

## Abstract (arXiv)

Active inference is a process theory of the brain that states that all living organisms infer actions in order to minimize their (expected) free energy. However, current experiments are limited to predefined, often discrete, state spaces. In this paper we use recent advances in deep learning to learn the state space and approximate the necessary probability distributions to engage in active inference.