Abstract
In the present paper, we conduct a comparative evaluation of a multitude of information-seeking domains, using two well-known but fundamentally different algorithms for policy learning: GP-SARSA and DQN. Our goal is to gain an understanding of how the nature of such domains influences performance. Our results indicate several main domain characteristics that play an important role in policy learning performance in terms of task success rates.
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Papangelis, A., Ultes, S., Stylianou, Y. (2019). Domain Complexity and Policy Learning in Task-Oriented Dialogue Systems. In: Eskenazi, M., Devillers, L., Mariani, J. (eds) Advanced Social Interaction with Agents . Lecture Notes in Electrical Engineering, vol 510. Springer, Cham. https://doi.org/10.1007/978-3-319-92108-2_8
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DOI: https://doi.org/10.1007/978-3-319-92108-2_8
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