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Mastering the Game of Go Without Human Knowledge

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Mastering the Game of Go Without Human Knowledge Mastering the Game of Go Without Human Knowledge Lead: Liam Hinzman Facilitators: Tahseen Shabab and Susan Cheng AlphaGo Zero Overview ● Brief History of AI in Games ● What is Go and Why Should You Care? ● How AlphaGo Zero Works ● Results ● Discussion Minimax Heuristics Reduces search depth Deep Blue ● 126 million positions per second ● Hand-designed Heuristics The Game of Go Go is Incredibly Complex Go is Hard for Computers How AlphaGo Zero Works Monte-Carlo Tree Search Residual Network Policy Iteration Monte-Carlo Tree Search (MCTS) Monte-Carlo Tree Search (MCTS) MCTS: Advantages ● Aheuristic ● Online-search ● Works well on large trees MCTS: Disadvantages ● Many simulation are required ● No generalization between similar states ● Performance is dependent on “rollout” policy MCTS in AlphaGo Zero Upper Confidence Bound for Trees (UCT) Upper Confidence Bound for Trees (UCT) Upper Confidence Bound for Trees (UCT) s State a Action Q(s, a) Expected Reward P(s, a) Policy N(s, a) # of state visits cpuct Hyperparameter AlphaGo Zero’s Network Architecture Residual Layer Dual Heads Training Self-play Worker Training Worker Evaluator 휋’ > 휋 How AlphaGo Zero Chooses a Move 1600 1/휏 Simulations 휋 ~ N Self-Play Workers Training Worker Evaluator 400 Games 55% Win Rate MCTS in AlphaGo Zero 5 Minute Break AlphaGo Zero vs AlphaGo Supervised learning + self-play Entirely self-play Input is hand-crafted features Input is game board Two networks Single network Rollouts were used No rollouts Results Learning Stages Ladders AlphaZero AlphaGo Zero’s Gift Discussion Discussion How can the AlphaGo Zero algorithm be extended to different games? How can the sample efficiency of AlphaGo Zero be improved? A very stable training environment is need for the algorithm. Can this be alleviated to let AlphaZero applied to real-world problems? Resources Mastering the Game of Go without Human Knowledge David Silver 2017 NIPS Talk ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero David Silver’s PhD Thesis: Reinforcement Learning and Simulation-Based Search in Computer Go A Brief History of Game AI Up To AlphaGo - Andrey Kurenkov AlphaGo Zero Demystified - Dylan Djian.
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