Quality-based Rewards for Monte-Carlo Tree Search Simulations

@inproceedings{Pepels2014QualitybasedRF,
  title={Quality-based Rewards for Monte-Carlo Tree Search Simulations},
  author={Tom Pepels and Mandy J. W. Tak and Marc Lanctot and Mark H. M. Winands},
  booktitle={ECAI},
  year={2014}
}
Monte-Carlo Tree Search is a best-first search technique based on simulations to sample the state space of a decision-making problem. In games, positions are evaluated based on estimates obtained from rewards of numerous randomized play-outs. Generally, rewards from play-outs are discrete values representing the outcome of the game (loss, draw, or win), e.g… CONTINUE READING