Efficient Controller Learning from Human Preferences and Numerical Data Via Multi-Modal Surrogate Models
Efficient Controller Learning from Human Preferences and Numerical Data Via Multi-Modal Surrogate Models explores A framework for efficiently tuning control policies by combining numerical data with human preferences using multi-modal surrogate models.. Commercial viability score: 5/10 in Control Systems Optimization.
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