  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. To address this, we present the \emph{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.
