IEEE Member-only icon Going beyond Games: Towards Decision Making in The Real-world - IEEE CoG2022 Keynote III Going beyond Games: Towards Decision Making in The Real-world - IEEE CoG2022 Keynote III

Going beyond Games: Towards Decision Making in The Real-world - IEEE CoG2022 Keynote III

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The Keynote III of the 2022 IEEE Conference on Games was given by Yuandong Tian, entitled, Going beyond Games: Towards Decision Making in The Real-world.

Deep Reinforcement Learning (DRL), a smart search technique that dynamically improves its policy and value estimation based on observation given previous data, has shown human-level or even super-human performance for games such as Go, chess, and StarCraft.

On the other hand, new challenges emerge when applying DRL in real-world applications, such as effective integration with current working systems, learning representation of large state and action spaces, or even redefining the temporal structure of sequential decision-making.

In this talk, the speaker covers our recent works that include learning initial solutions to the existing solver, learning state representations, or even learning the structure of sequential decision itself.

The Keynote III of the 2022 IEEE Conference on Games was given by Yuandong Tian, entitled, Going beyond Games: Towards Decision Making in The Real-world.

Deep Reinforcement Learning (DRL), a smart search technique that dynamically improves its...

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