6 research outputs found
Accelerating and Improving AlphaZero Using Population Based Training
AlphaZero has been very successful in many games. Unfortunately, it still
consumes a huge amount of computing resources, the majority of which is spent
in self-play. Hyperparameter tuning exacerbates the training cost since each
hyperparameter configuration requires its own time to train one run, during
which it will generate its own self-play records. As a result, multiple runs
are usually needed for different hyperparameter configurations. This paper
proposes using population based training (PBT) to help tune hyperparameters
dynamically and improve strength during training time. Another significant
advantage is that this method requires a single run only, while incurring a
small additional time cost, since the time for generating self-play records
remains unchanged though the time for optimization is increased following the
AlphaZero training algorithm. In our experiments for 9x9 Go, the PBT method is
able to achieve a higher win rate for 9x9 Go than the baselines, each with its
own hyperparameter configuration and trained individually. For 19x19 Go, with
PBT, we are able to obtain improvements in playing strength. Specifically, the
PBT agent can obtain up to 74% win rate against ELF OpenGo, an open-source
state-of-the-art AlphaZero program using a neural network of a comparable
capacity. This is compared to a saturated non-PBT agent, which achieves a win
rate of 47% against ELF OpenGo under the same circumstances.Comment: accepted by AAAI2020 as oral presentation. In this version,
supplementary materials are adde
SAI: A sensible artificial intelligence that plays with handicap and targets high scores in 9x9 Go
We develop a new framework for the game of Go to target a high score, and thus a perfect play. We integrate this framework into the Monte Carlo tree search - policy iteration learning pipeline introduced by Google DeepMind with AlphaGo. Training on 9×9 Go produces a superhuman Go player, thus proving that this framework is stable and robust. We show that this player can be used to effectively play with both positional and score handicap. We develop a family of agents that can target high scores against any opponent, recover from very severe disadvantage against weak opponents, and avoid suboptimal moves