Self-Play Learning Without a Reward Metric
Machine Learning
2019-12-17 v1 Machine Learning
Abstract
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 perform any quantitative balancing of reward components. We demonstrate that this system learns optimal play in a comparable amount of time to AlphaZero on a sample game.
Keywords
Cite
@article{arxiv.1912.07557,
title = {Self-Play Learning Without a Reward Metric},
author = {Dan Schmidt and Nick Moran and Jonathan S. Rosenfeld and Jonathan Rosenthal and Jonathan Yedidia},
journal= {arXiv preprint arXiv:1912.07557},
year = {2019}
}
Comments
6 pages, 4 figures