# Scaling active inference

arXiv: https://arxiv.org/abs/1911.10601 · HTML: https://mlsanity.com/paper/1911.10601/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2019.

## Built on by (most cited first, 30 of 30)

- [Active Inference in Robotics and Artificial Agents: Survey and Challenges](https://mlsanity.com/paper/2112.01871/) (2021)
- [pymdp: A Python library for active inference in discrete state spaces](https://mlsanity.com/paper/2201.03904/) (2022)
- [World Models and Predictive Coding for Cognitive and Developmental Robotics: Frontiers and Challenges](https://mlsanity.com/paper/2301.05832/) (2023)
- [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)
- [Backprop-Free Reinforcement Learning with Active Neural Generative Coding](https://mlsanity.com/paper/2107.07046/) (2021)
- [Understanding the Origin of Information-Seeking Exploration in Probabilistic Objectives for Control](https://mlsanity.com/paper/2103.06859/) (2021)
- [R-AIF: Solving Sparse-Reward Robotic Tasks from Pixels with Active Inference and World Models](https://mlsanity.com/paper/2409.14216/) (2024)
- [Prior Preference Learning from Experts:Designing a Reward with Active Inference](https://mlsanity.com/paper/2101.08937/) (2021)
- [Active Inference and Epistemic Value in Graphical Models](https://mlsanity.com/paper/2109.00541/) (2021)
- [Resolving uncertainty on the fly: Modeling adaptive driving behavior as active inference](https://mlsanity.com/paper/2311.06417/) (2023)
- [Measuring Surprise in the Wild](https://mlsanity.com/paper/2305.07733/) (2023)
- [Integrating cognitive map learning and active inference for planning in ambiguous environments](https://mlsanity.com/paper/2308.08307/) (2023)
- [Value of Information and Reward Specification in Active Inference and POMDPs](https://mlsanity.com/paper/2408.06542/) (2024)
- [Online reinforcement learning with sparse rewards through an active inference capsule](https://mlsanity.com/paper/2106.02390/) (2021)
- [Intrinsic Rewards for Exploration without Harm from Observational Noise: A Simulation Study Based on the Free Energy Principle](https://mlsanity.com/paper/2405.07473/) (2024)
- [Active Inference Tree Search in Large POMDPs](https://mlsanity.com/paper/2103.13860/) (2021)
- [Deep Active Inference for Pixel-Based Discrete Control: Evaluation on the Car Racing Problem](https://mlsanity.com/paper/2109.04155/) (2021)
- [A Neural Active Inference Model of Perceptual-Motor Learning](https://mlsanity.com/paper/2211.10419/) (2022)
- [Free Energy Projective Simulation (FEPS): Active inference with interpretability](https://mlsanity.com/paper/2411.14991/) (2024)
- [Realising Active Inference in Variational Message Passing: the Outcome-blind Certainty Seeker](https://mlsanity.com/paper/2104.11798/) (2021)
- [Mapping Husserlian phenomenology onto active inference](https://mlsanity.com/paper/2208.09058/) (2022)
- [Safe and Interpretable Estimation of Optimal Treatment Regimes](https://mlsanity.com/paper/2310.15333/) (2023)
- [Contemplative Artificial Intelligence](https://mlsanity.com/paper/2504.15125/) (2025)
- [Prior preferences in active inference agents: soft, hard, and goal shaping](https://mlsanity.com/paper/2512.03293/) (2025)
- [Active Dynamical Prospection: Modeling Mental Simulation as Particle Filtering for Sensorimotor Control during Pathfinding](https://mlsanity.com/paper/2103.07966/) (2021)
- [AIDA: An Active Inference-based Design Agent for Audio Processing Algorithms](https://mlsanity.com/paper/2112.13366/) (2021)
- [Online Pareto-Optimal Decision-Making for Complex Tasks using Active Inference](https://mlsanity.com/paper/2406.11984/) (2024)
- [EcoNet: Multiagent Planning and Control Of Household Energy Resources Using Active Inference](https://mlsanity.com/paper/2512.21343/) (2025)
- [Distributional Active Inference](https://mlsanity.com/paper/2601.20985/) (2026)

## Abstract (arXiv)

In reinforcement learning (RL), agents often operate in partially observed and uncertain environments. Model-based RL suggests that this is best achieved by learning and exploiting a probabilistic model of the world. 'Active inference' is an emerging normative framework in cognitive and computational neuroscience that offers a unifying account of how biological agents achieve this. On this framework, inference, learning and action emerge from a single imperative to maximize the Bayesian evidence for a niched model of the world. However, implementations of this process have thus far been restricted to low-dimensional and idealized situations. Here, we present a working implementation of active inference that applies to high-dimensional tasks, with proof-of-principle results demonstrating efficient exploration and an order of magnitude increase in sample efficiency over strong model-free baselines. Our results demonstrate the feasibility of applying active inference at scale and highlight the operational homologies between active inference and current model-based approaches to RL.