# Lifting DecPOMDPs for Nanoscale Systems -- A Work in Progress

arXiv: https://arxiv.org/abs/2110.09152 · HTML: https://mlsanity.com/paper/2110.09152/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 29 references that are in the map.
- First posted on arXiv 18 Oct 2021.

## Builds on (most cited first, 3 of 3)

- [Tractability through Exchangeability: A New Perspective on Efficient Probabilistic Inference](https://mlsanity.com/paper/1401.1247/) (2014)
- [Generating and Sampling Orbits for Lifted Probabilistic Inference](https://mlsanity.com/paper/1903.04672/) (2019)
- [Exploring Unknown Universes in Probabilistic Relational Models](https://mlsanity.com/paper/2001.02021/) (2020)

## Abstract (arXiv)

DNA-based nanonetworks have a wide range of promising use cases, especially in the field of medicine. With a large set of agents, a partially observable stochastic environment, and noisy observations, such nanoscale systems can be modelled as a decentralised, partially observable, Markov decision process (DecPOMDP). As the agent set is a dominating factor, this paper presents (i) lifted DecPOMDPs, partitioning the agent set into sets of indistinguishable agents, reducing the worst-case space required, and (ii) a nanoscale medical system as an application. Future work turns to solving and implementing lifted DecPOMDPs.