A parallel learning cellular automata for combinatorial optimization problems

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Reinforcement learning is a class of learning methodologies in which the controller (or agent) adapts based on external feedback from the random environment. We present a theoretic model of stochastic learning cellular automata (SLCA) as a model of reinforcement learning systems. The SLCA is an extended model of traditional cellular automata, defined as a stochastic cellular automaton with its random environment. There are three rule spaces for the SLCA: parallel, sequential and mixture. We especially study the parallel SLCA with a genetic operator and apply it to the combinatorial optimization problems. The computer simulations of graph partition problems show that the convergence of SLCA is better than the parallel mean field algorithm.

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