11 results on '"Aja Huang"'
Search Results
2. Discovering faster matrix multiplication algorithms with reinforcement learning.
- Author
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Alhussein Fawzi, Matej Balog, Aja Huang, Thomas Hubert, Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov 0001, Francisco J. R. Ruiz, Julian Schrittwieser, Grzegorz Swirszcz, David Silver, Demis Hassabis, and Pushmeet Kohli
- Published
- 2022
- Full Text
- View/download PDF
3. Grandmaster level in StarCraft II using multi-agent reinforcement learning.
- Author
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Oriol Vinyals, Igor Babuschkin, Wojciech M. Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H. Choi, Richard Powell, Timo Ewalds, Petko Georgiev, Junhyuk Oh, Dan Horgan, Manuel Kroiss, Ivo Danihelka, Aja Huang, Laurent Sifre, Trevor Cai, John P. Agapiou, Max Jaderberg, Alexander Sasha Vezhnevets, Rémi Leblond, Tobias Pohlen, Valentin Dalibard, David Budden, Yury Sulsky, James Molloy, Tom Le Paine, çaglar Gülçehre, Ziyu Wang 0001, Tobias Pfaff, Yuhuai Wu, Roman Ring, Dani Yogatama, Dario Wünsch, Katrina McKinney, Oliver Smith, Tom Schaul, Timothy P. Lillicrap, Koray Kavukcuoglu, Demis Hassabis, Chris Apps, and David Silver
- Published
- 2019
- Full Text
- View/download PDF
4. Mastering the game of Go without human knowledge.
- Author
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David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, Yutian Chen 0001, Timothy P. Lillicrap, Fan Hui, Laurent Sifre, George van den Driessche 0002, Thore Graepel, and Demis Hassabis
- Published
- 2017
- Full Text
- View/download PDF
5. Mastering the game of Go with deep neural networks and tree search.
- Author
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David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche 0002, Julian Schrittwieser, Ioannis Antonoglou, Vedavyas Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy P. Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis
- Published
- 2016
- Full Text
- View/download PDF
6. Bayesian Optimization in AlphaGo.
- Author
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Yutian Chen 0001, Aja Huang, Ziyu Wang 0001, Ioannis Antonoglou, Julian Schrittwieser, David Silver, and Nando de Freitas
- Published
- 2018
7. Grandmaster level in StarCraft II using multi-agent reinforcement learning
- Author
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Tom Schaul, David Silver, James Molloy, Junhyuk Oh, Katrina McKinney, Oriol Vinyals, David H. Choi, Junyoung Chung, Tobias Pohlen, Dani Yogatama, Tobias Pfaff, Demis Hassabis, Michael Mathieu, Dan Horgan, Ivo Danihelka, Igor Babuschkin, Dario Wünsch, Tom Le Paine, Yury Sulsky, Wojciech Marian Czarnecki, Rémi Leblond, Ziyu Wang, Andrew Dudzik, Trevor Cai, Chris Apps, Yuhuai Wu, David Budden, Valentin Dalibard, Timo Ewalds, Oliver Smith, John P. Agapiou, Aja Huang, Roman Ring, Petko Georgiev, Max Jaderberg, Koray Kavukcuoglu, Alexander Vezhnevets, Caglar Gulcehre, Manuel Kroiss, Laurent Sifre, Richard E. Powell, and Timothy P. Lillicrap
- Subjects
Matching (statistics) ,Multidisciplinary ,Computer science ,ComputingMilieux_PERSONALCOMPUTING ,02 engineering and technology ,010501 environmental sciences ,League ,01 natural sciences ,Domain (software engineering) ,Video Games ,Artificial Intelligence ,Human–computer interaction ,Stepping stone ,0202 electrical engineering, electronic engineering, information engineering ,Humans ,Learning ,Reinforcement learning ,Learning methods ,020201 artificial intelligence & image processing ,Relevance (information retrieval) ,Reinforcement learning algorithm ,Reinforcement, Psychology ,0105 earth and related environmental sciences - Abstract
Many real-world applications require artificial agents to compete and coordinate with other agents in complex environments. As a stepping stone to this goal, the domain of StarCraft has emerged as an important challenge for artificial intelligence research, owing to its iconic and enduring status among the most difficult professional esports and its relevance to the real world in terms of its raw complexity and multi-agent challenges. Over the course of a decade and numerous competitions1-3, the strongest agents have simplified important aspects of the game, utilized superhuman capabilities, or employed hand-crafted sub-systems4. Despite these advantages, no previous agent has come close to matching the overall skill of top StarCraft players. We chose to address the challenge of StarCraft using general-purpose learning methods that are in principle applicable to other complex domains: a multi-agent reinforcement learning algorithm that uses data from both human and agent games within a diverse league of continually adapting strategies and counter-strategies, each represented by deep neural networks5,6. We evaluated our agent, AlphaStar, in the full game of StarCraft II, through a series of online games against human players. AlphaStar was rated at Grandmaster level for all three StarCraft races and above 99.8% of officially ranked human players.
- Published
- 2019
8. Discovering faster matrix multiplication algorithms with reinforcement learning
- Author
-
Alhussein Fawzi, Matej Balog, Aja Huang, Thomas Hubert, Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, Francisco J. R. Ruiz, Julian Schrittwieser, Grzegorz Swirszcz, David Silver, Demis Hassabis, and Pushmeet Kohli
- Subjects
Multidisciplinary - Abstract
Improving the efficiency of algorithms for fundamental computations can have a widespread impact, as it can affect the overall speed of a large amount of computations. Matrix multiplication is one such primitive task, occurring in many systems—from neural networks to scientific computing routines. The automatic discovery of algorithms using machine learning offers the prospect of reaching beyond human intuition and outperforming the current best human-designed algorithms. However, automating the algorithm discovery procedure is intricate, as the space of possible algorithms is enormous. Here we report a deep reinforcement learning approach based on AlphaZero1 for discovering efficient and provably correct algorithms for the multiplication of arbitrary matrices. Our agent, AlphaTensor, is trained to play a single-player game where the objective is finding tensor decompositions within a finite factor space. AlphaTensor discovered algorithms that outperform the state-of-the-art complexity for many matrix sizes. Particularly relevant is the case of 4 × 4 matrices in a finite field, where AlphaTensor’s algorithm improves on Strassen’s two-level algorithm for the first time, to our knowledge, since its discovery 50 years ago2. We further showcase the flexibility of AlphaTensor through different use-cases: algorithms with state-of-the-art complexity for structured matrix multiplication and improved practical efficiency by optimizing matrix multiplication for runtime on specific hardware. Our results highlight AlphaTensor’s ability to accelerate the process of algorithmic discovery on a range of problems, and to optimize for different criteria.
- Published
- 2021
9. Move Evaluation in Go Using Deep Convolutional Neural Networks.
- Author
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Chris J. Maddison, Aja Huang, Ilya Sutskever, and David Silver
- Published
- 2015
10. Mastering the game of Go without human knowledge
- Author
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Karen Simonyan, Timothy P. Lillicrap, Demis Hassabis, George van den Driessche, Ioannis Antonoglou, Adrian Bolton, Yutian Chen, Fan Hui, Aja Huang, Thomas Hubert, David Silver, Arthur Guez, Laurent Sifre, Julian Schrittwieser, Lucas Baker, Thore Graepel, and Matthew Lai
- Subjects
0209 industrial biotechnology ,Multidisciplinary ,Artificial neural network ,Computer science ,business.industry ,Supervised learning ,Monte Carlo tree search ,02 engineering and technology ,Tabula rasa ,020901 industrial engineering & automation ,Games, Recreational ,0202 electrical engineering, electronic engineering, information engineering ,Humans ,Reinforcement learning ,Unsupervised learning ,Domain knowledge ,020201 artificial intelligence & image processing ,Neural Networks, Computer ,Supervised Machine Learning ,Artificial intelligence ,business ,Reinforcement, Psychology ,Computer Go ,Software ,Unsupervised Machine Learning - Abstract
A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. The tree search in AlphaGo evaluated positions and selected moves using deep neural networks. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from self-play. Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGo’s own move selections and also the winner of AlphaGo’s games. This neural network improves the strength of the tree search, resulting in higher quality move selection and stronger self-play in the next iteration. Starting tabula rasa, our new program AlphaGo Zero achieved superhuman performance, winning 100–0 against the previously published, champion-defeating AlphaGo. Starting from zero knowledge and without human data, AlphaGo Zero was able to teach itself to play Go and to develop novel strategies that provide new insights into the oldest of games. To beat world champions at the game of Go, the computer program AlphaGo has relied largely on supervised learning from millions of human expert moves. David Silver and colleagues have now produced a system called AlphaGo Zero, which is based purely on reinforcement learning and learns solely from self-play. Starting from random moves, it can reach superhuman level in just a couple of days of training and five million games of self-play, and can now beat all previous versions of AlphaGo. Because the machine independently discovers the same fundamental principles of the game that took humans millennia to conceptualize, the work suggests that such principles have some universal character, beyond human bias.
- Published
- 2017
11. Mastering the game of Go with deep neural networks and tree search
- Author
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John Nham, David Silver, Ilya Sutskever, Demis Hassabis, George van den Driessche, Arthur Guez, Dominik Grewe, Marc Lanctot, Thore Graepel, Laurent Sifre, Chris J. Maddison, Nal Kalchbrenner, Ioannis Antonoglou, Timothy P. Lillicrap, Koray Kavukcuoglu, Julian Schrittwieser, Madeleine Leach, Aja Huang, Veda Panneershelvam, and Sander Dieleman
- Subjects
Game mechanics ,Multidisciplinary ,business.industry ,Monte Carlo tree search ,ComputingMilieux_PERSONALCOMPUTING ,Combinatorial game theory ,02 engineering and technology ,021001 nanoscience & nanotechnology ,Bioinformatics ,computer.software_genre ,General video game playing ,General game playing ,0202 electrical engineering, electronic engineering, information engineering ,Reinforcement learning ,020201 artificial intelligence & image processing ,Artificial intelligence ,0210 nano-technology ,business ,Late Move Reductions ,Computer Go ,computer - Abstract
The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses ‘value networks’ to evaluate board positions and ‘policy networks’ to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of stateof-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.
- Published
- 2016
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