Abstract:
Research in robotics and A.I. aims at optimizing very specific task rewards. Intelligent animals have a high degree of curiosity, and recent

results have shown that instrumental reward optimization is a poor explanation for their behavior. We can show that to explain empirical

results from animals, we need to have the drive to optimize reward, a drive to reduce uncertainty, and a drive for positive cues. We then show examples in robotics where a more complex reward system provides benefits in learning.

Daddaoua, N., Lopes, . & Gottlieb, J. Intrinsically motivated oculomotor exploration guided by uncertainty reduction and conditioned

reinforcement in non-human primates. Sci Rep 6, 20202 (2016). https://doi.org/10.1038/srep20202

Lopes, M., Lang, T., Toussaint, M., & Oudeyer, P. Y. (2012). Exploration in model-based reinforcement learning by empirically estimating learning progress. In Advances in neural information processing systems (pp. 206-214).

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