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Machine Intelligence Sweden AB
Google’s AlphaGo...and a career in machine learning
Hans SalomonssonCTO
Machine Intelligence Sweden
Machine Intelligence Sweden AB
4 - 1
Machine Intelligence Sweden AB
GoViewed as the most complex of classical games due to its huge search space
b - no legal moves per positiond - game length (iterations)bd sequences of moves
chess: b = 35 d = 80go: b = 250 d = 150
Machine Intelligence Sweden AB
Search Space
Source: https://thenewstack.io/google-ai-beats-human-champion-complex-game-ever-invented/
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Monte Carlo Tree Search
Source: https://en.wikipedia.org/wiki/Monte_Carlo_tree_search
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Value Function vs Policy ApproximationDeep Q-learning approximates the action-value function Q(s, a) with an ANN
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Value Function vs Policy Approximation
Source: Silver, D. et al. Mastering the game of Go with deep neural networks and tree search. Nature 529, 484–489 (2016)
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Architecture Overview
Source: Silver, D. et al. Mastering the game of Go with deep neural networks and tree search. Nature 529, 484–489 (2016)
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MCTS in AlphaGo
Source: Silver, D. et al. Mastering the game of Go with deep neural networks and tree search. Nature 529, 484–489 (2016)
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MCTS in AlphaGo● Do MCTS until time budget is out, then select the action with the maximum
visit count● AlphaGo resigns when its overall evaluation drops below an estimated 10%
probability of winning the game
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HardwareThe final version of AlphaGo used 40 search threads, 48 CPUs, and 8 GPUs.
Source: Silver, D. et al. Mastering the game of Go with deep neural networks and tree search. Nature
529, 484–489 (2016)
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AlphaZeroGoal: Remove all human knowledge, i.e.
● No human features● No human data
In addition to this
● Single neural network● Simpler search (no MCTS)
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Source: Silver, D. et al. Mastering the Game of Go without Human Knowledge. Nature 550, 354–359 (2017)
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How to train “Policy Network”
Source: Silver, D. et al. Mastering the Game of Go without Human Knowledge. Nature 550, 354–359 (2017)
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How to train “Value Network”
Source: Silver, D. et al. Mastering the Game of Go without Human Knowledge. Nature 550, 354–359 (2017)
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ResultSource: Silver, D. et al. Mastering the Game of Go without Human Knowledge. Nature 550, 354–359 (2017)
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Takeaways ● Superhuman performance at GO with DL and RL● NN flexible tool● Balance trade-off speed and accuracy ● Some things are surprising - test things!● Hardware important
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About us
19
Devdatt DubhashiCEO Professor CSE Chalmers UniversityLeads the Machine Learning and Algorithms GroupPh.D. Cornell University, U.S.A.
Hans SalomonssonCTO Has built AI systems since 2010 M.Sc. Complex Adaptive SystemsB.Sc. Engineering PhysicsB.Sc. Industrial and Financial Mgmt
Anton KloekData ScientistM.Sc. Algorithms, Languages and LogicB.Sc. Computer Science and Engg.
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