GAIG Game AI Research Group @ QMUL

NeurIPS 2019 Competition: The MineRL Competition on Sample Efficient Reinforcement Learning using Human Priors

2019
Guss, William H and Codel, Cayden and Hofmann, Katja and Houghton, Brandon and Kuno, Noboru and Milani, Stephanie and Mohanty, Sharada and Perez Liebana, Diego and Salakhutdinov, Ruslan and Topin, Nicholay and others

Abstract

Though deep reinforcement learning has led to breakthroughs in many difficult domains, these successes have required an ever-increasing number of samples. As state-ofthe-art reinforcement learning (RL) systems require an exponentially increasing number of samples, their development is restricted to a continually shrinking segment of the AI community. Likewise, many of these systemss cannot be applied to real-world problems, where environment samples are expensive. Resolution of these limitations requires new, sample-efficient methods. To facilitate research in this direction, we propose the MineRL Competition on Sample Efficient Reinforcement Learning using Human Priors. The primary goal of the competition is to foster the development of algorithms which can efficiently leverage human demonstrations to drastically reduce the number of samples needed to solve complex, hierarchical, and sparse environments. To that end, we introduce: (1) the Minecraft ObtainDiamond task, a sequential decision making environment requiring long-term planning, hierarchical control, and efficient exploration methods; and (2) the MineRL-v0 dataset, a large-scale collection of over 60 million state-action pairs of human demonstrations that can be resimulated into embodied agent trajectories with arbitrary modifications to game state and visuals. Participants will compete to develop systems which solve the ObtainDiamond task with a limited number of samples from the environment simulator, Malmo. The competition is structured into two rounds in which competitors are provided several paired versions of the dataset and environment with different game textures and shaders. At the end of each round, competitors will submit containerized versions of their learning algorithms to the AICrowd platform where they will then be trained from scratch on a hold-out dataset-environment pair for a total of 4-days on a pre-specified hardware platform. Each submission will then be automatically ranked according to the final performance of the trained agent.
Arxiv: https://arxiv.org/abs/1904.10079 

Cite this work

@article{guss2019minerl,
author= {Guss, William H and Codel, Cayden and Hofmann, Katja and Houghton, Brandon and Kuno, Noboru and Milani, Stephanie and Mohanty, Sharada and Perez Liebana, Diego and Salakhutdinov, Ruslan and Topin, Nicholay and others},
title= {{NeurIPS 2019 Competition: The MineRL Competition on Sample Efficient Reinforcement Learning using Human Priors}},
year= {2019},
journal= {{arXiv:1904.10079}},
abstract= {Though deep reinforcement learning has led to breakthroughs in many difficult domains, these successes have required an ever-increasing number of samples. As state-ofthe-art reinforcement learning (RL) systems require an exponentially increasing number of samples, their development is restricted to a continually shrinking segment of the AI community. Likewise, many of these systemss cannot be applied to real-world problems, where environment samples are expensive. Resolution of these limitations requires new, sample-efficient methods. To facilitate research in this direction, we propose the MineRL Competition on Sample Efficient Reinforcement Learning using Human Priors. The primary goal of the competition is to foster the development of algorithms which can efficiently leverage human demonstrations to drastically reduce the number of samples needed to solve complex, hierarchical, and sparse environments. To that end, we introduce: (1) the Minecraft ObtainDiamond task, a sequential decision making environment requiring long-term planning, hierarchical control, and efficient exploration methods; and (2) the MineRL-v0 dataset, a large-scale collection of over 60 million state-action pairs of human demonstrations that can be resimulated into embodied agent trajectories with arbitrary modifications to game state and visuals. Participants will compete to develop systems which solve the ObtainDiamond task with a limited number of samples from the environment simulator, Malmo. The competition is structured into two rounds in which competitors are provided several paired versions of the dataset and environment with different game textures and shaders. At the end of each round, competitors will submit containerized versions of their learning algorithms to the AICrowd platform where they will then be trained from scratch on a hold-out dataset-environment pair for a total of 4-days on a pre-specified hardware platform. Each submission will then be automatically ranked according to the final performance of the trained agent. },
}

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