  This paper proposes a paradigm shift for affective computing by viewing the
affect modeling task as a reinforcement learning process. According to our
proposed framework the context (environment) and the actions of an agent define
the common representation that interweaves behavior and affect. To realise this
framework we build on recent advances in reinforcement learning and use a
modified version of the Go-Explore algorithm which has showcased supreme
performance in hard exploration tasks. In this initial study, we test our
framework in an arcade game by training Go-Explore agents to both play
optimally and attempt to mimic human demonstrations of arousal. We vary the
degree of importance between optimal play and arousal imitation and create
agents that can effectively display a palette of affect and behavioral
patterns. Our Go-Explore implementation not only introduces a new paradigm for
affect modeling; it empowers believable AI-based game testing by providing
agents that can blend and express a multitude of behavioral and affective
patterns.
