Mastering Atari, Go, chess and shogi by planning with a learned model
Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, Timothy Lillicrap, David Silver
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- Mastering Atari, Go, chess and shogi by planning with a learned model→Reinforcement Learning: An IntroductionLegacy edge — source detail unavailable
- Mastering Atari, Go, chess and shogi by planning with a learned model→Mastering the game of Go with deep neural networks and tree searchLegacy edge — source detail unavailable
- Mastering Atari, Go, chess and shogi by planning with a learned model→Mastering the game of Go without human knowledgeLegacy edge — source detail unavailable
- Mastering Atari, Go, chess and shogi by planning with a learned model→Reinforcement Learning: An IntroductionLegacy edge — source detail unavailable
- Mastering Atari, Go, chess and shogi by planning with a learned model→Markov Decision Processes: Discrete Stochastic Dynamic Programming.Legacy edge — source detail unavailable
- Mastering Atari, Go, chess and shogi by planning with a learned model→Markov Decision ProcessesLegacy edge — source detail unavailable
- Mastering Atari, Go, chess and shogi by planning with a learned model→A general reinforcement learning algorithm that masters chess, shogi, and Go through self…Legacy edge — source detail unavailable
- Mastering Atari, Go, chess and shogi by planning with a learned model→Grandmaster level in StarCraft II using multi-agent reinforcement learningLegacy edge — source detail unavailable
- Mastering Atari, Go, chess and shogi by planning with a learned model→Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learningLegacy edge — source detail unavailable
- Mastering Atari, Go, chess and shogi by planning with a learned model→Markov Decision Processes: Discrete Stochastic Dynamic ProgrammingLegacy edge — source detail unavailable
- Mastering Atari, Go, chess and shogi by planning with a learned model→Rainbow: Combining Improvements in Deep Reinforcement LearningLegacy edge — source detail unavailable
- Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning→Reinforcement Learning: An IntroductionLegacy edge — source detail unavailable
- Mastering the game of Go with deep neural networks and tree search→Reinforcement Learning: An IntroductionLegacy edge — source detail unavailable
- Rainbow: Combining Improvements in Deep Reinforcement Learning→Reinforcement Learning: An IntroductionLegacy edge — source detail unavailable
- Mastering the game of Go without human knowledge→Reinforcement Learning: An IntroductionLegacy edge — source detail unavailable
- Mastering the game of Go without human knowledge→Mastering the game of Go with deep neural networks and tree searchLegacy edge — source detail unavailable
- A general reinforcement learning algorithm that masters chess, shogi, and Go through self…→Mastering the game of Go with deep neural networks and tree searchLegacy edge — source detail unavailable
- A general reinforcement learning algorithm that masters chess, shogi, and Go through self…→Mastering the game of Go without human knowledgeLegacy edge — source detail unavailable
- Grandmaster level in StarCraft II using multi-agent reinforcement learning→Reinforcement Learning: An IntroductionLegacy edge — source detail unavailable
- Grandmaster level in StarCraft II using multi-agent reinforcement learning→Mastering the game of Go with deep neural networks and tree searchLegacy edge — source detail unavailable
- Grandmaster level in StarCraft II using multi-agent reinforcement learning→A general reinforcement learning algorithm that masters chess, shogi, and Go through self…Legacy edge — source detail unavailable
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| Artificial Intelligence in Games | Computer Science |
| Educational Games and Gamification | Psychology |
| Reinforcement Learning in Robotics | Computer Science |
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