# Successor Representation Active Inference

arXiv: https://arxiv.org/abs/2207.09897 · HTML: https://mlsanity.com/paper/2207.09897/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 50 references that are in the map.
- First posted on arXiv 20 Jul 2022.

## Builds on (most cited first, 19 of 19)

- [Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review](https://mlsanity.com/paper/1805.00909/) (2018)
- [Count-Based Exploration with the Successor Representation](https://mlsanity.com/paper/1807.11622/) (2018)
- [Active inference on discrete state-spaces: a synthesis](https://mlsanity.com/paper/2001.07203/) (2020)
- [Eigenoption Discovery through the Deep Successor Representation](https://mlsanity.com/paper/1710.11089/) (2017)
- [Deep active inference agents using Monte-Carlo methods](https://mlsanity.com/paper/2006.04176/) (2020)
- [The free energy principle for action and perception: A mathematical review](https://mlsanity.com/paper/1705.09156/) (2017)
- [Sophisticated Inference](https://mlsanity.com/paper/2006.04120/) (2020)
- [Deep Active Inference as Variational Policy Gradients](https://mlsanity.com/paper/1907.03876/) (2019)
- [Reinforcement Learning through Active Inference](https://mlsanity.com/paper/2002.12636/) (2020)
- [On the Relationship Between Active Inference and Control as Inference](https://mlsanity.com/paper/2006.12964/) (2020)
- [A free energy principle for a particular physics](https://mlsanity.com/paper/1906.10184/) (2019)
- [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)
- [Understanding the Origin of Information-Seeking Exploration in Probabilistic Objectives for Control](https://mlsanity.com/paper/2103.06859/) (2021)
- [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)
- [How particular is the physics of the free energy principle?](https://mlsanity.com/paper/2105.11203/) (2021)
- [How to build a cognitive map: insights from models of the hippocampal formation](https://mlsanity.com/paper/2202.01682/) (2022)
- [A Mathematical Walkthrough and Discussion of the Free Energy Principle](https://mlsanity.com/paper/2108.13343/) (2021)

## Built on by (most cited first, 3 of 3)

- [Free Energy Projective Simulation (FEPS): Active inference with interpretability](https://mlsanity.com/paper/2411.14991/) (2024)
- [Learning Successor Features with Distributed Hebbian Temporal Memory](https://mlsanity.com/paper/2310.13391/) (2023)
- [Efficient search of active inference policy spaces using k-means](https://mlsanity.com/paper/2209.02550/) (2022)

## Abstract (arXiv)

Recent work has uncovered close links between between classical reinforcement learning algorithms, Bayesian filtering, and Active Inference which lets us understand value functions in terms of Bayesian posteriors. An alternative, but less explored, model-free RL algorithm is the successor representation, which expresses the value function in terms of a successor matrix of expected future state occupancies. In this paper, we derive the probabilistic interpretation of the successor representation in terms of Bayesian filtering and thus design a novel active inference agent architecture utilizing successor representations instead of model-based planning. We demonstrate that active inference successor representations have significant advantages over current active inference agents in terms of planning horizon and computational cost. Moreover, we demonstrate how the successor representation agent can generalize to changing reward functions such as variants of the expected free energy.