Using fuzzy reinforcement learning for power control in wireless transmitters
David Vengerov, H.R. Berenji · 2003
Fuzzy set theory was recently shown to be an effective tool for generalizing the learned experience between similar states in reinforcement learning problems with large or continuous state spaces. In our previous work (2001) we presented the first convergence proof for an algorithm combining fuzzy sets and reinforcement learning. In this paper we apply our algorithm to a very challenging wireless power control problem characterized by heavily delayed rewards combined with several sources of randomness. The results show a considerable improvement in performance as compared to the optimal constant power transmission.