Evolving integrated controllers for autonomous learning robots using dynamic neural networks

In 1994, Yamauchi and Beer (1994) attempted to evolve a dynamic neural network as a control system for a simulated agent capable of performing learning behaviour. They tried to evolve an integrated network, i.e. not modularized; this attempt failed. They ended up having to use independent evolution of separate controller modules, arbitrarily partitioned by the researcher. Moreover, they “provided” the agents with hard-wired reinforcement signals.The model we describe in this paper demonstrates that it is possible to evolve an integrated dynamic neural network that successfully controls the behaviour of a khepera robot engaged in a simple learning task. We show that dynamic neural networks, based on leaky-integrator neuron, shaped by evolution, appear to be able to integrate reactive and learned behaviour with an integrated control system which also benefits from its own evolved reinforcement signal.

E. Tuci, I. Harvey and M. Quinn (2002): Evolving integrated controllers for autonomous learning robots using dynamic neural networks. Proceedings of The Seventh International Conference on the Simulation of Adaptive Behavior (SAB’02), 4-9 August 2002, Edinburgh, UK. MIT Press, ISBN: 0262582171.

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