Data assimilation technique for social agent-based simulation by using reinforcement learning

Indoor localisation and tracking in a residential home setting are envisaged to play an essential role in smart home environments. As it is hard to obtain a large number of labelled data, semi-supervised learning with Reinforcement Learning is ...

Agent-based simulation is useful for studying human activity and their interactions in smart environments. Existing agent-based simulations are mostly offline tools that do not utilize real time information of smart environments. In previous work we ...

Recent advancements in reinforcement learning confirm that reinforcement learning techniques can solve large scale problems leading to high quality autonomous decision making. It is a matter of time until we will see large scale applications of ...