On the Importance of Exploration for Real Life Learned Algorithms

Autoren: S. Gracla, C. Bockelmann, A. Dekorsy
Kurzfassung:

The quality of data driven learning algorithms scales significantly with the quality of data available. One of the most straight-forward ways to generate good data is to sample or explore the data source intelligently. Smart sampling can reduce the cost of gaining samples, reduce computation cost in learning, and enable the learning algorithm to adapt to unforeseen events. In this paper, we teach three Deep Q-Networks (DQN) with different exploration strategies to solve a problem of puncturing ongoing transmissions for \urllc messages. We demonstrate the efficiency of two adaptive exploration candidates, variance-based and Maximum Entropy-based exploration, compared to the standard, simple Epsilon-Greedy exploration approach. 

Dokumenttyp: Konferenzbeitrag
Veröffentlichung: IEEE, 28. - 30. Juli 2022
Konferenz: IEEE SPAWC 2022
Dateien:
On the Importance of Exploration for Real Life Learned Algorithms
00_on_exploration_preprint.pdf500 KB
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Zuletzt aktualisiert am 09.11.2022 von S. Gracla
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