A connection has recently been drawn between Dynamic Optimization Problems (DOPs) and Reinforcement Learning Problems (RLPs) where they can be seen as subsets of a broader class of Sequential Decision-Making Problems (SDMPs). SDMPs require new decisions on an ongoing basis. Typically the underlying environment changes between decisions. The SDMP view is useful as it allows the unified space to be explored. Solutions can be designed for characteristics of problem instances using algorithms from either community. Little has been done on comparing algorithm performance across these communities, particularly under real-world resource constraints.
|Title of host publication||2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016|
|Number of pages||8|
|Publication status||Published - 9 Feb 2017|
|Event||2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 - Athens, Greece|
Duration: 6 Dec 2016 → 9 Dec 2016
|Conference||2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016|
|Period||6/12/16 → 9/12/16|