MineRL: A Large-Scale Dataset of Minecraft Demonstrations

Guss, William H.; Houghton, Brandon; Topin, Nicholay; Wang, Phillip; Codel, Cayden; Veloso, Manuela; Salakhutdinov, Ruslan

doi:10.48550/arxiv.1907.13440

Cited by 12 publications

(20 citation statements)

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“…Limited Human Demonstrations and Environment Interactions. The competition provides 60 million frames of human demonstrations [8], but only 211 trajectories are collected in the ObtainDiamond environment 6 . Besides, participants are restricted to use at most 8 million frames of environment interactions.…”

Section: The Minerl Competitionmentioning

confidence: 99%

“…It has been shown that SQIL can be interpreted as a regularized variant of behavioral cloning that uses a sparse prior to encourage imitation. Therefore, the agent can easily obtain the Logs as human does after training 8 .…”

Section: The Choptree Agentmentioning

confidence: 99%

“…The MineRL competition [8,6,25,9,5,7] has become a popular competition promoting sample-efficient reinforcement learning and imitation learning algorithms. There are lots of relevant studies.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, the MineRL competition [8,6,25,9,5,7] has been introduced in order to promote researches in sample-efficient reinforcement learning and imitation learning that leverage human demonstrations to solve the complex ObtainDiamond task in the Minecraft game. Specifically, an agent starts off from a random position on a randomly-generated Minecraft map with the goal to obtain a Diamond, which can only be accomplished by mining materials and crafting necessary tools.…”

Section: Introductionmentioning

confidence: 99%

“…For example, we test deep ResNet[10], but the performance has not improved much, while the training time has increased a lot 8. We also test the Generative Adversarial Imitation Learning (GAIL) algorithm[12], but it is not as stable as SQIL.…”

mentioning

confidence: 99%

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

Mao¹,

Wang²,

Hao³

et al. 2021

Preprint

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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.

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Section: The Minerl Competitionmentioning

confidence: 99%

Section: The Choptree Agentmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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

Mao¹,

Wang²,

Hao³

et al. 2021

Preprint

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ChemGymRL: A customizable interactive framework for reinforcement learning for digital chemistry

Beeler,

Subramanian,

Sprague

et al. 2024

Digital Discovery

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Fast and accurate DNASeq Variant Calling workflow composed of LUSH toolkit

Wang¹,

Zhang²,

Wang³

et al. 2023

Preprint

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BackgroundWhole genome sequencing (WGS) is becoming increasingly prevalent for molecular diagnosis, staging and prognosis because of its declining costs and the ability to detect nearly all genes associated with a patient’s disease. The currently widely accepted variant calling pipeline, GATK, is limited in terms of its computational speed and efficiency, which cannot meet the growing analysis needs.MethodsIn this study, we propose a fast and accurate DNASeq variant calling workflow that is purely composed of tools from LUSH toolkit. The LUSH pipeline is highly optimized for the WGS pipeline based on SOAPnuke, BWA and GATK which can be deployed on any general-purpose CPU-based computing system. We validated the accuracy, speed and scalability of the LUSH pipeline on several standard WGS datasets.ResultsOur test results show that the LUSH pipeline and the GATK pipeline are highly consistent in terms of accuracy, achieving over 99% precision and recall on NA12878. For speed, the LUSH pipeline completes 30x WGS data in 1.6 hours, which is about 17x faster than the GATK pipeline. From BAM to VCF, LUSH_HC even takes only 12 minutes, about 76x faster than GATK. Moreover, the LUSH pipeline shows favorable scalability in terms of thread and sequencing depth.ConclusionThe LUSH pipeline provides far superior computational speed to GATK while maintaining a high level of accuracy comparable to that of GATK, which greatly facilitates bedside analysis of acute patients, large-scale cohort data analysis, and variant calling in crop breeding programs.

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MineRL: A Large-Scale Dataset of Minecraft Demonstrations

Cited by 12 publications

References 0 publications

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

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

ChemGymRL: A customizable interactive framework for reinforcement learning for digital chemistry

Fast and accurate DNASeq Variant Calling workflow composed of LUSH toolkit

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