Abstract: Policy Gradient is a policy-based reinforcement learning algorithm that approximates the optimal policy through a parametric function. The algorithm classifies the observations by softmax ...

Abstract: Minimizing sum-of-nonconvex functions is the critical respect in decentralized optimization, which is also the focus for machine learning. In this paper, we propose a decentralized conjugate ...

1D thermal resistance network for CPU cold plate optimization, calibrated against CFD simulations. Enables rapid parametric design studies to identify thermal bottlenecks and evaluate design ...