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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Emotion-Based Intrinsic Motivation for Reinforcement Learning Agents
verfasst von : Pedro Sequeira, Francisco S. Melo, Ana Paiva
Erschienen in: Affective Computing and Intelligent Interaction
Verlag: Springer Berlin Heidelberg
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In this paper, we propose an adaptation of four common appraisal dimensions that evaluate the relation of an agent with its environment into reward features within an
intrinsically motivated reinforcement learning
framework. We show that, by optimizing the relative weights of such features for a given environment, the agents attain a greater degree of fitness while overcoming some of their perceptual limitations. This optimization process resembles the evolutionary adaptive process that living organisms are subject to. We illustrate the application of our method in several simulated foraging scenarios.