# On Predictive planning and counterfactual learning in active inference

arXiv: https://arxiv.org/abs/2403.12417 · HTML: https://mlsanity.com/paper/2403.12417/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 37 references that are in the map.
- First posted on arXiv 19 Mar 2024.

## Builds on (most cited first, 13 of 13)

- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey](https://mlsanity.com/paper/2006.11371/) (2020)
- [Deep Variational Reinforcement Learning for POMDPs](https://mlsanity.com/paper/1806.02426/) (2018)
- [Active inference on discrete state-spaces: a synthesis](https://mlsanity.com/paper/2001.07203/) (2020)
- [Deep active inference agents using Monte-Carlo methods](https://mlsanity.com/paper/2006.04176/) (2020)
- [Sophisticated Inference](https://mlsanity.com/paper/2006.04120/) (2020)
- [Deep Active Inference](https://mlsanity.com/paper/1709.02341/) (2017)
- [Whence the Expected Free Energy?](https://mlsanity.com/paper/2004.08128/) (2020)
- [pymdp: A Python library for active inference in discrete state spaces](https://mlsanity.com/paper/2201.03904/) (2022)
- [On efficient computation in active inference](https://mlsanity.com/paper/2307.00504/) (2023)
- [Active Inference for Stochastic Control](https://mlsanity.com/paper/2108.12245/) (2021)
- [Designing explainable artificial intelligence with active inference: A framework for transparent introspection and decision-making](https://mlsanity.com/paper/2306.04025/) (2023)
- [Active Inference and Intentional Behaviour](https://mlsanity.com/paper/2312.07547/) (2023)

## Built on by (most cited first, 4 of 4)

- [Expected Free Energy-based Planning as Variational Inference](https://mlsanity.com/paper/2504.14898/) (2025)
- [A memory-based active inference model of DishBrain-like adaptive behaviour](https://mlsanity.com/paper/2508.06980/) (2025)
- [Expected Free Energy-based Planning as Variational Inference](https://mlsanity.com/paper/2606.20658/) (2026)
- [Sophisticated Policies from Epistemic Priors](https://mlsanity.com/paper/2607.19518/) (2026)

## Abstract (arXiv)

Given the rapid advancement of artificial intelligence, understanding the foundations of intelligent behaviour is increasingly important. Active inference, regarded as a general theory of behaviour, offers a principled approach to probing the basis of sophistication in planning and decision-making. In this paper, we examine two decision-making schemes in active inference based on 'planning' and 'learning from experience'. Furthermore, we also introduce a mixed model that navigates the data-complexity trade-off between these strategies, leveraging the strengths of both to facilitate balanced decision-making. We evaluate our proposed model in a challenging grid-world scenario that requires adaptability from the agent. Additionally, our model provides the opportunity to analyze the evolution of various parameters, offering valuable insights and contributing to an explainable framework for intelligent decision-making.