Comparison of NEAT and HyperNEAT on a Strategic Decision-Making Problem
نویسندگان
چکیده
Neuroevolution is a useful machine learning approach for problems with limited domain knowledge, but it has not done well with strategic decision-making problems, where the correct action varies sharply as the agent moves across states. Two promising neuroevolution algorithms are NeuroEvolution of Augmenting Topologies (NEAT) and its extension, HyperNEAT. We compare the performance of these two algorithms on a benchmark problem, Keepaway Soccer, that requires strategic decision-making. Our results demonstrate that HyperNEAT outperforms NEAT on a simple instance of the problem but that its advantage disappears when the problem is complicated.
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