# Rationally inattentive control of Markov processes

arXiv: https://arxiv.org/abs/1502.03762 · HTML: https://mlsanity.com/paper/1502.03762/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2015.

## Built on by (most cited first, 6 of 6)

- [Deciding What to Model: Value-Equivalent Sampling for Reinforcement Learning](https://mlsanity.com/paper/2206.02072/) (2022)
- [On Rate-Distortion Theory in Capacity-Limited Cognition & Reinforcement Learning](https://mlsanity.com/paper/2210.16877/) (2022)
- [Bayesian Reinforcement Learning with Limited Cognitive Load](https://mlsanity.com/paper/2305.03263/) (2023)
- [Satisficing Exploration for Deep Reinforcement Learning](https://mlsanity.com/paper/2407.12185/) (2024)
- [Entropy-Regularized Partially Observed Markov Decision Processes](https://mlsanity.com/paper/2112.12255/) (2021)
- [Simultaneous Perception-Action Design via Invariant Finite Belief Sets](https://mlsanity.com/paper/2109.05073/) (2021)

## Abstract (arXiv)

The article poses a general model for optimal control subject to information constraints, motivated in part by recent work of Sims and others on information-constrained decision-making by economic agents. In the average-cost optimal control framework, the general model introduced in this paper reduces to a variant of the linear-programming representation of the average-cost optimal control problem, subject to an additional mutual information constraint on the randomized stationary policy. The resulting optimization problem is convex and admits a decomposition based on the Bellman error, which is the object of study in approximate dynamic programming. The theory is illustrated through the example of information-constrained linear-quadratic-Gaussian (LQG) control problem. Some results on the infinite-horizon discounted-cost criterion are also presented.