Self-Play Learning Without a Reward Metric
arXiv:1912.07557
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.
arXiv comment6 pages, 4 figures
References in corpus (4)
- Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
- Accelerating Self-Play Learning in Go
- Ranked Reward: Enabling Self-Play Reinforcement Learning for Combinatorial Optimization
- SAI: a Sensible Artificial Intelligence that plays with handicap and targets high scores in 9x9 Go (extended version)