A Transfer Learning Algorithm to Improve the Convergence Rate and Accuracy in Cellular Learning Automata
Subject Areas : electrical and computer engineering
Seyyed Amir Hadi
Minoofam
1
( Karaj Islamic Azad University)
Azam
Bastanfard
2
M. R.
Keyvanpour
3
Keywords: Cellular learning automata, convergence rate, transfer learning, knowledge transfer,
Abstract :
Cellular learning automaton is an intelligent model as a composition of cellular automaton and learning automaton. In this study, an extended algorithm of cellular learning automata is proposed based on transfer learning as the TL-CLA algorithm. In this algorithm, transfer learning is used as an approach for computation deduction and minimizing the learning cycle. The proposed algorithm is an extended model based on merit function and attitude vector for transfer learning. In the TL-CLA algorithm, the value of the merit function is computed based on the local environment, and the value of the attitude vector is calculated based on the global environment. When these two measures get the threshold values, the transfer of action probabilities causes the transfer learning from the source CLA to the destination CLA. The experimental results show that the proposed TL-CLA model leads to increment the convergence accuracy as 2.7% and 2.2% in two actions and multi-action standard environments, respectively. The improvements in convergence rate are also 8% and 2% in these two environments. The TL-CLA could be applied in knowledge transfer from learning one task to learning another similar task
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