On the Importance of Exploration for Real Life Learned Algorithms

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

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, Oulu, Finnland, 28. - 30. Juli 2022
Konferenz: 23rd IEEE International Workshop on Signal Processing Advances in Wireless Communications
On the Importance of Exploration for Real Life Learned Algorithms
00_on_exploration_preprint.pdf500 KB
Zuletzt aktualisiert am 12.05.2023 von S. Gracla
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