Discovering diverse solutions in deep reinforcement learning by maximizing state-action-based mutual information.

Reinforcement learning algorithms are typically limited to learning a single solution for a specified task, even though diverse solutions often exist. Recent studies showed that learning a set of diverse solutions is beneficial because diversity enables robust few-shot adaptation. Although existing methods learn diverse solutions by using the mutual information as unsupervised rewards, such an approach often suffers from the bias of the gradient estimator induced by value function approximation.

Sample-efficient parameter exploration of the powder film drying process using experiment-based Bayesian optimization.

Parameter optimization is a long-standing challenge in various production processes. Particularly, powder film forming processes entail multiscale and multiphysical phenomena, each of which is usually controlled by a combination of several parameters. Therefore, it is difficult to optimize the parameters either by numerical-model-based analysis or by "brute force" experiment-based exploration.

Analysis and estimation of cutting-temperature distribution during end milling in relation to orthopedic surgery.

Living bone must be cut before performing arthroplasty. For example, the distal part of the femur and the proximal part of the tibia must be cut to perform total knee arthroplasty. Osteocytes begin to necrose when the cutting temperature during such procedures exceeds 50 degrees C.