Affectively Framework: Towards Human-like Affect-Based Agents

--Game environments offer a unique opportunity for training virtual agents due to their interactive nature, which provides diverse play traces and affect labels. Despite their potential, no reinforcement learning framework incorporates human affect models as part of their observation space or reward mechanism. T o address this, we present the Affectively Framework, a set of Open-AI Gym environments that integrate affect as part of the observation space. This paper introduces the framework and its three game environments and provides baseline experiments to validate its effectiveness and potential. Video games are ideal stimuli for research in Affective Computing [1] for several reasons. Firstly, the user is free to play in many different ways, leading to diversity in their play traces and emotional experiences [2].