An Evaluation Methodology for Interactive Reinforcement Learning with Simulated Users.

Interactive reinforcement learning methods utilise an external information source to evaluate decisions and accelerate learning. Previous work has shown that human advice could significantly improve learning agents' performance. When evaluating reinforcement learning algorithms, it is common to repeat experiments as parameters are altered or to gain a sufficient sample size.

Supporting Regional Aged Care Nursing Staff to Manage Residents' Behavioural and Psychological Symptoms of Dementia, in Real Time, Using the Nurses' Behavioural Assistant (NBA): A Pilot Site 'End-User Attitudes' Trial.

Background: This regional pilot site 'end-user attitudes' study explored nurses' experiences and impressions of using the Nurses' Behavioural Assistant (NBA) (a knowledge-based, interactive ehealth system) to assist them to better respond to behavioural and psychological symptoms of dementia (BPSD) and will be reported here.

Methods: Focus groups were conducted, followed by a four-week pilot site 'end-user attitudes' trial of the NBA at a regional aged care residential facility (ACRF). Brief interviews were conducted with consenting nursing staff.