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EnglishQuality of Experience (QoE) assessment in video games is notorious for its burdensomeness. Employing human subjects to understand network impact on the perceived gaming QoE presents major drawbacks in terms of resources requirement, results interpretability and poor transferability across different games. To overcome these shortcomings, we propose to substitute human players with artificial agents trained with state-of-the-art Deep Reinforcement Learning techniques. Equivalently to traditional QoE assessment, we measure the in-game score achieved by an artificial agent for the game of Doom for varying network parameters. Our results show that the proposed methodology can be applied to understand fine-grained impact of network conditions on gaming experience while opening a lot of new opportunities for network operators and game developers.
Leveraging AI players for QoE estimation in cloud gaming / Sviridov, German; Beliard, Cedric; Simon, Gwendal; Bianco, Andrea; Giaccone, Paolo; Rossi, Dario. - ELETTRONICO. - (2020), pp. 1282-1283. ((Intervento presentato al convegno IEEE INFOCOM 2020 - Demo Session tenutosi a Toronto (Canada) nel July 2020 [10.1109/INFOCOMWKSHPS50562.2020.9162732].
| Titolo: | Leveraging AI players for QoE estimation in cloud gaming | |
| Autori: | ||
| Data di pubblicazione: | 2020 | |
| Abstract: | Quality of Experience (QoE) assessment in video games is notorious for its burdensomeness. Employing human subjects to understand network impact on the perceived gaming QoE presents major drawbacks in terms of resources requirement, results interpretability and poor transferability across different games. To overcome these shortcomings, we propose to substitute human players with artificial agents trained with state-of-the-art Deep Reinforcement Learning techniques. Equivalently to traditional QoE assessment, we measure the in-game score achieved by an artificial agent for the game of Doom for varying network parameters. Our results show that the proposed methodology can be applied to understand fine-grained impact of network conditions on gaming experience while opening a lot of new opportunities for network operators and game developers. | |
| Appare nelle tipologie: | 4.3 Poster |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| 1570623456.pdf | Articolo Principale | 2. Post-print / Author's Accepted Manuscript | PUBBLICO - Tutti i diritti riservati | Visibile a tuttiVisualizza/Apri |
| 09162732.pdf | Published version | 2a Post-print versione editoriale / Version of Record | Non Pubblico - Accesso privato/ristretto | Administrator Richiedi una copia |
http://hdl.handle.net/11583/2802852
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