Inverse design of thermally active composite via policy-transferred reinforcement learning

Key Points

• This research aims to improve the inverse design process for thermally active composites using policy-transferred reinforcement learning.
• Utilized policy-transferred reinforcement learning to enhance sample efficiency in design.
• Focused on programmability and transformation capabilities of thermally active composites.
• Achieved efficient inverse design with programmable capabilities in 4D shape transformations.