Emotional evaluation of bandit problems
In this paper, we discuss an approach to evaluate decisions made during a multi-armed bandit learning experiment. Usually, the results of machine learning algorithms applied on multi-armed bandit scenarios are rated in terms of earned reward and optimal decisions taken. These criteria are valuable for objective comparison in finite experiments. But learning algorithms used in real scenarios, for example in robotics, need to have instantaneous criteria to evaluate their actual decisions taken. To overcome this problem, in our approach each decision updates the Zürich model which emulates the human sense of feeling secure and aroused. Combining these two feelings results in an emotional evaluation of decision policies and could be used to model the emotional state of an intelligent agent.