Whats Worth Memorizing: Attribute-Based Planning for DEC-POMDPs.

摘要

Current algorithms for decentralized partially observable Markov decision processes (DEC-POMDPs) require a large amount of memory to produce high quality plans. To combat this, existing methods optimize a set of finite-state controllers with an arbitrary amount of fixed memory. While this works well for some problems, in general, scalability and solution quality remain limited. As an alternative, we propose remembering some attributes that summarize key aspects of an agents action and observation history. These attributes are often simple to determine, provide a well-motivated choice of controller size and focus the solution search on important components of agent histories. We show that for a range of DEC-POMDPs such attribute-based representation improves plan quality and scalability.