Lifting DecPOMDPs for Nanoscale Systems -- A Work in Progress
This work addresses scalability issues in decentralized control for nanoscale systems, but it is incremental as it builds on existing DecPOMDP frameworks without solving them.
The paper tackles the problem of modeling nanoscale systems with many agents by introducing lifted DecPOMDPs, which partition agents into indistinguishable sets to reduce worst-case space requirements, and applies this to a nanoscale medical system.
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.