A structurally dynamic cellular automaton with coincidence-based edge creation, de-inforcement, and pre-wired reward gradients can navigate small graphs to rewards after a single training run, but optimality and generality are not established.
S., Tanner, J., and Itti, L
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A Computational Model of Learning and Memory Using Structurally Dynamic Cellular Automata
A structurally dynamic cellular automaton with coincidence-based edge creation, de-inforcement, and pre-wired reward gradients can navigate small graphs to rewards after a single training run, but optimality and generality are not established.