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 m...

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Published in2024 12th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) pp. 144 - 148
Main Authors Barthet, Matthew, Gallotta, Roberto, Khalifa, Ahmed, Liapis, Antonios, Yannakakis, Georgios N.
Format Conference Proceeding
LanguageEnglish
Published IEEE 15.09.2024
Subjects
Affective computing
baselines
Conferences
Games
Reinforcement learning
Software packages
Training
Virtual environments
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Summary: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 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.
DOI:10.1109/ACIIW63320.2024.00029