论文标题
用行为克隆玩Minecraft
Playing Minecraft with Behavioural Cloning
论文作者
论文摘要
Minerl 2019竞赛通过使用人类游戏的数据集和环境的限制数量来挑战参与者培训样品效率的代理人玩Minecraft。我们通过预测人类参与者将采取的行动并在最终排名中排名第五，从而通过行为克隆来完成这项任务。尽管是一种简单的算法，但我们观察到这种方法的性能可能会根据训练的停止而有很大差异。在本文中，我们详细介绍了对竞争的提交，进行了进一步的实验，以研究培训的绩效如何变化，并研究不同的工程决策如何影响这些结果。
MineRL 2019 competition challenged participants to train sample-efficient agents to play Minecraft, by using a dataset of human gameplay and a limit number of steps the environment. We approached this task with behavioural cloning by predicting what actions human players would take, and reached fifth place in the final ranking. Despite being a simple algorithm, we observed the performance of such an approach can vary significantly, based on when the training is stopped. In this paper, we detail our submission to the competition, run further experiments to study how performance varied over training and study how different engineering decisions affected these results.
