Avoid Overfitting in Deep Reinforcement Learning: Increasing Robustness Through Decentralized Control

Schilling M (2021)
In: Artificial Neural Networks and Machine Learning – ICANN 2021. 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part IV. Farkaš I, Masulli P, Otte S, Wermter S (Eds); Lecture Notes in Computer Science. Cham: Springer International Publishing: 638-649.

Konferenzbeitrag | Veröffentlicht | Englisch
 
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Herausgeber*in
Farkaš, Igor; Masulli, Paolo; Otte, Sebastian; Wermter, Stefan
Erscheinungsjahr
2021
Titel des Konferenzbandes
Artificial Neural Networks and Machine Learning – ICANN 2021. 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part IV
forms.conference.field.series_title_volume.series_title.label
Lecture Notes in Computer Science
Seite(n)
638-649
ISBN
978-3-030-86379-1
eISBN
978-3-030-86380-7
ISSN
0302-9743
eISSN
1611-3349
Page URI
https://pub.uni-bielefeld.de/record/2958667

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Schilling M. Avoid Overfitting in Deep Reinforcement Learning: Increasing Robustness Through Decentralized Control. In: Farkaš I, Masulli P, Otte S, Wermter S, eds. Artificial Neural Networks and Machine Learning – ICANN 2021. 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part IV. Lecture Notes in Computer Science. Cham: Springer International Publishing; 2021: 638-649.
Schilling, M. (2021). Avoid Overfitting in Deep Reinforcement Learning: Increasing Robustness Through Decentralized Control. In I. Farkaš, P. Masulli, S. Otte, & S. Wermter (Eds.), Lecture Notes in Computer Science. Artificial Neural Networks and Machine Learning – ICANN 2021. 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part IV (pp. 638-649). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-86380-7_52
Schilling, M. (2021). “Avoid Overfitting in Deep Reinforcement Learning: Increasing Robustness Through Decentralized Control” in Artificial Neural Networks and Machine Learning – ICANN 2021. 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part IV, Farkaš, I., Masulli, P., Otte, S., and Wermter, S. eds. Lecture Notes in Computer Science (Cham: Springer International Publishing), 638-649.
Schilling, M., 2021. Avoid Overfitting in Deep Reinforcement Learning: Increasing Robustness Through Decentralized Control. In I. Farkaš, et al., eds. Artificial Neural Networks and Machine Learning – ICANN 2021. 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part IV. Lecture Notes in Computer Science. Cham: Springer International Publishing, pp. 638-649.
M. Schilling, “Avoid Overfitting in Deep Reinforcement Learning: Increasing Robustness Through Decentralized Control”, Artificial Neural Networks and Machine Learning – ICANN 2021. 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part IV, I. Farkaš, et al., eds., Lecture Notes in Computer Science, Cham: Springer International Publishing, 2021, pp.638-649.
Schilling, M.: Avoid Overfitting in Deep Reinforcement Learning: Increasing Robustness Through Decentralized Control. In: Farkaš, I., Masulli, P., Otte, S., and Wermter, S. (eds.) Artificial Neural Networks and Machine Learning – ICANN 2021. 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part IV. Lecture Notes in Computer Science. p. 638-649. Springer International Publishing, Cham (2021).
Schilling, Malte. “Avoid Overfitting in Deep Reinforcement Learning: Increasing Robustness Through Decentralized Control”. Artificial Neural Networks and Machine Learning – ICANN 2021. 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part IV. Ed. Igor Farkaš, Paolo Masulli, Sebastian Otte, and Stefan Wermter. Cham: Springer International Publishing, 2021. Lecture Notes in Computer Science. 638-649.

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