# Self-Play Learning Without a Reward Metric

@article{Schmidt2019SelfPlayLW, title={Self-Play Learning Without a Reward Metric}, author={Dan Schmidt and N. Moran and Jonathan S. Rosenfeld and Jonathan Rosenthal and J. Yedidia}, journal={ArXiv}, year={2019}, volume={abs/1912.07557} }

The AlphaZero algorithm for the learning of strategy games via self-play, which has produced superhuman ability in the games of Go, chess, and shogi, uses a quantitative reward function for game outcomes, requiring the users of the algorithm to explicitly balance different components of the reward against each other, such as the game winner and margin of victory. We present a modification to the AlphaZero algorithm that requires only a total ordering over game outcomes, obviating the need to… Expand

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