Abstract
What is happiness for reinforcement learning agents? We seek a formal definition satisfying a list of desiderata. Our proposed definition of happiness is the temporal difference error, i.e. the difference between the value of the obtained reward and observation and the agent’s expectation of this value. This definition satisfies most of our desiderata and is compatible with empirical research on humans. We state several implications and discuss examples.
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Daswani, M., & Leike, J. (2015). A definition of happiness for reinforcement learning agents. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9205, pp. 231–240). Springer Verlag. https://doi.org/10.1007/978-3-319-21365-1_24
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