Investigates the efficiency of policy-transferred reinforcement learning in designing thermally active composites, suggesting advanced manufacturing techniques.
Policy-transferred reinforcement learning enables sample-efficient inverse design of thermally active composites with programmable 4D shape transformation.
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Lee et al. (2026) studied this question.
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