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Offline Reinforcement Learning Hands-On
Offline Reinforcement Learning (RL) aims to turn large datasets into powerful decision-making engines without any online interactions with the environment.
Exploring the fitness landscape of a realistic turbofan rotor blade optimization
The choices to be made in the setup of the optimization process strongly influence this mapping and thus are expected to have a profound influence on the achievable result.
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