On Measuring the Impact of Human Actions in the Machine Learning of a Board Game's Playing Policies

Dimitris Kalles

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On Measuring the Impact of Human Actions in the Machine Learning of a Board Game's Playing Policies

Dimitris Kalles

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Abstract

We investigate systematically the impact of human intervention in the training of computer players in a strategy board game. In that game, computer players utilise reinforcement learning with neural networks for evolving their playing strategies and demonstrate a slow learning speed. Human intervention can significantly enhance learning performance, but carry-ing it out systematically seems to be more of a problem of an integrated game development environment as opposed to automatic evolutionary learning.

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Dimitris Kalles. 2006-11-30. On Measuring the Impact of Human Actions in the Machine Learning of a Board Game's Playing Policies. https://arxiv.org/abs/cs/0611163

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