# Active Inference Meeting Energy-Efficient Control of Parallel and Identical Machines

arXiv: https://arxiv.org/abs/2406.09322 · HTML: https://mlsanity.com/paper/2406.09322/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 45 references that are in the map.
- First posted on arXiv 13 Jun 2024.

## Builds on (most cited first, 15 of 15)

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Monte Carlo Tree Search: A Review of Recent Modifications and Applications](https://mlsanity.com/paper/2103.04931/) (2021)
- [Iterative Amortized Inference](https://mlsanity.com/paper/1807.09356/) (2018)
- [Deep active inference agents using Monte-Carlo methods](https://mlsanity.com/paper/2006.04176/) (2020)
- [Active Inference in Robotics and Artificial Agents: Survey and Challenges](https://mlsanity.com/paper/2112.01871/) (2021)
- [Predictive Coding: Towards a Future of Deep Learning beyond Backpropagation?](https://mlsanity.com/paper/2202.09467/) (2022)
- [Amortized Variational Inference: When and Why?](https://mlsanity.com/paper/2307.11018/) (2023)
- [Active Inference and Behavior Trees for Reactive Action Planning and Execution in Robotics](https://mlsanity.com/paper/2011.09756/) (2020)
- [On efficient computation in active inference](https://mlsanity.com/paper/2307.00504/) (2023)
- [Active Exploration for Robotic Manipulation](https://mlsanity.com/paper/2210.12806/) (2022)
- [Control as Hybrid Inference](https://mlsanity.com/paper/2007.05838/) (2020)
- [Applications of the Free Energy Principle to Machine Learning and Neuroscience](https://mlsanity.com/paper/2107.00140/) (2021)
- [Bayesian brains and the R'enyi divergence](https://mlsanity.com/paper/2107.05438/) (2021)
- [Navigating Autonomous Vehicle on Unmarked Roads with Diffusion-Based Motion Prediction and Active Inference](https://mlsanity.com/paper/2406.00211/) (2024)

## Built on by (most cited first, 1 of 1)

- [Deep Active Inference Agents for Delayed and Long-Horizon Environments](https://mlsanity.com/paper/2505.19867/) (2025)

## Abstract (arXiv)

We investigate the application of active inference in developing energy-efficient control agents for manufacturing systems. Active inference, rooted in neuroscience, provides a unified probabilistic framework integrating perception, learning, and action, with inherent uncertainty quantification elements. Our study explores deep active inference, an emerging field that combines deep learning with the active inference decision-making framework. Leveraging a deep active inference agent, we focus on controlling parallel and identical machine workstations to enhance energy efficiency. We address challenges posed by the problem's stochastic nature and delayed policy response by introducing tailored enhancements to existing agent architectures. Specifically, we introduce multi-step transition and hybrid horizon methods to mitigate the need for complex planning. Our experimental results demonstrate the effectiveness of these enhancements and highlight the potential of the active inference-based approach.