Modular Neural Networks and Reinforcement Learning
نویسنده
چکیده
We investigate the effect of modular architecture in an artificial neural network for a reinforcement learning problem. Using the supervised backpropagation algorithm to solve a two-task problem, the network performance can be increased by using networks with modular structures. However, using a modular architecture to solve a two-task reinforcement learning problem will not increase the performance compared to a non-modular structure. We show that by combining a modular structure with a modular reward signal the network learns significantly faster.
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تاریخ انتشار 2004