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SEIHAI: A Sample-efficient Hierarchical AI for the MineRL Competition

arXiv.org Artificial Intelligence

The MineRL competition is designed for the development of reinforcement learning and imitation learning algorithms that can efficiently leverage human demonstrations to drastically reduce the number of environment interactions needed to solve the complex ObtainDiamond task with sparse rewards. To address the challenge, in this paper, we present SEIHAI, a Sample-efficient Hierarchical AI, that fully takes advantage of the human demonstrations and the task structure. Specifically, we split the task into several sequentially dependent subtasks, and train a suitable agent for each subtask using reinforcement learning and imitation learning. We further design a scheduler to select different agents for different subtasks automatically. SEIHAI takes the first place in the preliminary and final of the NeurIPS-2020 MineRL competition.


Scaling Imitation Learning in Minecraft

arXiv.org Artificial Intelligence

Imitation learning is a powerful family of techniques for learning sensorimotor coordination in immersive environments. We apply imitation learning to attain state-of-the-art performance on hard exploration problems in the Minecraft environment. We report experiments that highlight the influence of network architecture, loss function, and data augmentation. An early version of our approach reached second place in the MineRL competition at NeurIPS 2019. Here we report stronger results that can be used as a starting point for future competition entries and related research. Our code is available at https://github.com/amiranas/minerl_imitation_learning.


Playing Minecraft with Behavioural Cloning

arXiv.org Artificial Intelligence

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.


AI takes on popular Minecraft game in machine-learning contest

#artificialintelligence

Minecraft's open-ended play environment could be ideal for AI research, some researchers say.Credit: Microsoft To see the divide between the best artificial intelligence and the mental capabilities of a seven-year-old child, look no further than the popular video game Minecraft. A young human can learn how to find a rare diamond in the game after watching a 10-minute demonstration on YouTube. Artificial intelligence (AI) is nowhere close. But in a unique computing competition ending this month, researchers hope to shrink the gap between machine and child -- and in doing so, help to reduce the computing power needed to train AIs. Competitors may take up to four days and use no more than eight million steps to train their AIs to find a diamond.


AI Takes on Popular Minecraft Game in Machine-Learning Contest

#artificialintelligence

To see the divide between the best artificial intelligence and the mental capabilities of a seven-year-old child, look no further than the popular video game Minecraft. A young human can learn how to find a rare diamond in the game after watching a 10-minute demonstration on YouTube. Artificial intelligence (AI) is nowhere close. But in a unique computing competition ending this month, researchers hope to shrink the gap between machine and child--and in doing so, help to reduce the computing power needed to train AIs. Competitors may take up to four days and use no more than eight million steps to train their AIs to find a diamond.