Abstract: The present disclosure may provide parameterized hyperparameter partitioning in consideration of balance in partition size while preserving a property of a hypergraph necessary to apply deep reinforcement learning by reducing the large-size hypergraph, and may reduce the computational amount and capacity of an artificial neural network by reducing a graph.

Abstract: Disclosed are an apparatus and a method for reinforcement learning for semiconductor element position optimization based on semiconductor design data. According to the present disclosure, a learning environment may be constructed based on a user's semiconductor design data such that optimal positions of semiconductor elements are provided during a semiconductor design process through reinforcement learning using simulation.

Abstract: Disclosed are an apparatus and a method for reinforcement learning based on a user learning environment in semiconductor design. According to the present disclosure, a user may configure a learning environment in semiconductor design and may determine optimal positions of semiconductor elements and standard cells through reinforcement learning using simulation, and reinforcement learning may be performed based on the learning environment configured by the user, thereby automatically determining optimized semiconductor element positions in various environments.