# On Incorporating Forecasts into Linear State Space Model Markov Decision Processes

arXiv: https://arxiv.org/abs/2103.07533 · HTML: https://mlsanity.com/paper/2103.07533/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 22 references that are in the map.
- First posted on arXiv 12 Mar 2021.

## Built on by (most cited first, 2 of 2)

- [RangL: A Reinforcement Learning Competition Platform](https://mlsanity.com/paper/2208.00003/) (2022)
- [Solving Markov Decision Processes with Future Information via MPC](https://mlsanity.com/paper/2606.24991/) (2026)

## Abstract (arXiv)

Weather forecast information will very likely find increasing application in the control of future energy systems. In this paper, we introduce an augmented state space model formulation with linear dynamics, within which one can incorporate forecast information that is dynamically revealed alongside the evolution of the underlying state variable. We use the martingale model for forecast evolution (MMFE) to enforce the necessary consistency properties that must govern the joint evolution of forecasts with the underlying state. The formulation also generates jointly Markovian dynamics that give rise to Markov decision processes (MDPs) that remain computationally tractable. This paper is the first to enforce MMFE consistency requirements within an MDP formulation that preserves tractability.