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우리는 인간 같은 로봇 손으로 루빅스 큐브를 푸는 신경망 두 개를 훈련시켰습니다. 신경망은 전적으로 시뮬레이션으로 훈련되며, 강화 학습 코드와 동일합니다...
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We’ve trained a pair of neural networks to solve the Rubik’s Cube with a human-like robot hand. The neural networks are trained entirely in simulation, using the same reinforcement learning code as OpenAI Five paired with a new technique called Automatic Domain Randomization (ADR). The system can handle situations it never saw during training, such as being prodded by a stuffed giraffe. This shows that reinforcement learning isn’t just a tool for virtual tasks, but can solve physical-world problems requiring unprecedented dexterity.
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