The proximal partially-parallel splitting method (PPSM), originally proposed in Wang, Desai, and He (2017), is a hybrid …

-learning environment for portfolio reallocation, we train actor-critic agents (Proximal Policy Optimization (PPO) and Advantage Actor …

and traffic. Our approach integrates graph neural networks to encode spatial problem structure and proximal policy …

deploy a Proximal Policy Optimization (PPO) agent that incorporates deep learning outputs as part of its observation space …

In this paper, we introduce an inexact regularized proximal Newton method (IRPNM) that does not require any line search …’ knowledge, is a novel contribution for proximal Newton methods. To highlight the efficiency of our approach, we provide …

scheduling in line-less mobile assembly systems. The proposed multi agent deep reinforcement learning algorithm uses proximal …