# Bayesian policy selection using active inference

arXiv: https://arxiv.org/abs/1904.08149 · HTML: https://mlsanity.com/paper/1904.08149/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2019.

## Built on by (most cited first, 12 of 12)

- [Active Inference in Robotics and Artificial Agents: Survey and Challenges](https://mlsanity.com/paper/2112.01871/) (2021)
- [The Free Energy Principle for Perception and Action: A Deep Learning Perspective](https://mlsanity.com/paper/2207.06415/) (2022)
- [R-AIF: Solving Sparse-Reward Robotic Tasks from Pixels with Active Inference and World Models](https://mlsanity.com/paper/2409.14216/) (2024)
- [Applications of the Free Energy Principle to Machine Learning and Neuroscience](https://mlsanity.com/paper/2107.00140/) (2021)
- [Supervised structure learning](https://mlsanity.com/paper/2311.10300/) (2023)
- [Active Inference for Stochastic Control](https://mlsanity.com/paper/2108.12245/) (2021)
- [Learning Policies for Continuous Control via Transition Models](https://mlsanity.com/paper/2209.08033/) (2022)
- [Deep Active Inference for Pixel-Based Discrete Control: Evaluation on the Car Racing Problem](https://mlsanity.com/paper/2109.04155/) (2021)
- [Prior preferences in active inference agents: soft, hard, and goal shaping](https://mlsanity.com/paper/2512.03293/) (2025)
- [Dynamic planning in hierarchical active inference](https://mlsanity.com/paper/2402.11658/) (2024)
- [Distributional Active Inference](https://mlsanity.com/paper/2601.20985/) (2026)
- [Optimizing Neurorobot Policy under Limited Demonstration Data through Preference Regret](https://mlsanity.com/paper/2604.03523/) (2026)

## Abstract (arXiv)

Learning to take actions based on observations is a core requirement for artificial agents to be able to be successful and robust at their task. Reinforcement Learning (RL) is a well-known technique for learning such policies. However, current RL algorithms often have to deal with reward shaping, have difficulties generalizing to other environments and are most often sample inefficient. In this paper, we explore active inference and the free energy principle, a normative theory from neuroscience that explains how self-organizing biological systems operate by maintaining a model of the world and casting action selection as an inference problem. We apply this concept to a typical problem known to the RL community, the mountain car problem, and show how active inference encompasses both RL and learning from demonstrations.