![]() The competition is structured into two rounds in which competitors are provided several paired versions of the dataset and environment with different game textures. Participants will compete to develop systems which solve the ObtainDiamond task with a limited number of samples from the environment simulator, Malmo. 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 trajectories with arbitrary modifications to game state and visuals. 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 facilitate research in this direction, we introduce the MineRL Competition on Sample Efficient Reinforcement Learning using Human Priors. Resolution of these limitations requires new, sample-efficient methods. applied to real-world problems, where environment samples are expensive. Likewise, many of these systems cannot be. As state-of-the-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. Though deep reinforcement learning has led to breakthroughs in many difficult domains, these successes have required an ever-increasing number of samples. For further resources related to this article, please visit the WIREs website. doi: 10.1002/wcs.1278 CONFLICT OF INTEREST: The author has declared no conflicts of interest for this article. While computers have surpassed human expertise in a wide variety of games, open challenges remain and research focuses on identifying and developing new successful algorithmic foundations. ![]() These approaches have generalized successfully to additional games. Key developments include minimax search in chess, machine learning from self-play in backgammon, and Monte Carlo tree search in Go. Game playing research has identified several simple core algorithms that provide successful foundations, with development focused on the challenges of defeating human experts in specific games. Game playing provides clearly defined arenas within which computational approaches can be readily compared to human expertise through head-to-head competition and other benchmarks. Game playing has been a core domain of artificial intelligence research since the beginnings of the field. The tournament committee encourages new participants for the 2009 tournament, and will be happy to assist on technical issues. And although some programs are clearly superior to others, it is possible to write a reasonably competitive AI in a few months, especially since the Metaforge API eliminates need to write a user interface. Among participants there is not yet agreement on issues as fundamental as the importance of recursion in move selection. Stratego AI is at present a wide open field. The annual Computer Stratego World Championship selects the best Stratego-playing program. Bluffing plays an important role in this game. Therefore, when one piece tries to capture an opponent's piece, it might get captured itself. The players choose a custom setup of pieces at the start of the game, without showing the opponent the rank of each piece. ![]() It is a game of war with the objective to capture the opponent's flag. Stratego is a game of imperfect information that was first patented in 1960 by the Milton Badley Company (Collins, 2008).
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