Energy Regularized RNNs for Solving Non-Stationary Bandit Problems
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Abstract (arXiv)
We consider a Multi-Armed Bandit problem in which the rewards are non-stationary and are dependent on past actions and potentially on past contexts. At the heart of our method, we employ a recurrent neural network, which models these sequences. In order to balance between exploration and exploitation, we present an energy minimization term that prevents the neural network from becoming too confident in support of a certain action. This term provably limits the gap between the maximal and minimal probabilities assigned by the network. In a diverse set of experiments, we demonstrate that our method is at least as effective as methods suggested to solve the sub-problem of Rotting Bandits, and can solve intuitive extensions of various benchmark problems. We share our implementation at https://github.com/rotmanmi/Energy-Regularized-RNN.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 13 of its 29 references that are in the map. First posted on arXiv 12 Mar 2023.
Builds on
13 of 29 references.
References · 13
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Citing papers per year: 2023: 1.
1 paper.