Abstract
vioft2nntf2t|tblJournal|Abstract_paper|0xf4ff62df120000000f97020001000900
Machine learning schemes can be employed in cognitive radio systems to intelligently locate the spectrum holes with some knowledge about the operating environment. In this paper, we formulate a variation of Actor Critic Learning algorithm known as Continuous Actor Critic Learning Automaton (CACLA) and compare this scheme with Actor Critic Learning scheme and existing Q–learning scheme. Simulation results show that our CACLA scheme has lesser execution time and achieves higher throughput compared to other two schemes.
Authors
Dhananjay Kumar, Pavithra Hari, Panbhazhagi Selvaraj, Sharavanti Baskaran
Anna University, MIT Campus, Chennai, India
Keywords
Markov Decision Process, Reinforcement Learning, Q–learning, Actor–critic Learning, CACLA