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.
Research supported by the People for the Ethical Treatment of Reinforcement Learners http://petrl.org. See the extended technical report for omitted proofs and details about the data analysis [4].
Both authors contributed equally.
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Daswani, M., Leike, J. (2015). A Definition of Happiness for Reinforcement Learning Agents. In: Bieger, J., Goertzel, B., Potapov, A. (eds) Artificial General Intelligence. AGI 2015. Lecture Notes in Computer Science(), vol 9205. Springer, Cham. https://doi.org/10.1007/978-3-319-21365-1_24
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DOI: https://doi.org/10.1007/978-3-319-21365-1_24
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