error. Robust sampling is achieved at the game equilibrium. To accelerate the searching process, we adopt reinforcement … learning aided Monte Carlo trees search (MCTS). We apply our method to a car-following modeling problem, a complicated scenario …

literature, reinforcement learning (RL) has shown potential to quickly evaluate the reward- and value function of the Bellman …

) with family setups constraints is difficult to solve and proven to be NP-hard. To this end, we present a deep reinforcement … learning (DRL) approach to solve a PMSP considering family setups, aiming at minimizing the total tardiness. The PMSP is first …

This work presents a framework for interfacing a reinforcement learning algorithm with a finite element model in order … process of the titanium aluminide TNM-B1. The reinforcement learning algorithm interacts with the finite element model by … simulate the observed variations in deformation behavior of TNM-B1. The same reinforcement learning setup and reward function …

(GRG). We propose a novel reinforcement-learning-based framework, which consists of two graph neural networks trained in an …

algorithms have been increasingly applied in production to facilitate fast-paced operations. In particular deep reinforcement … learning frequently surpassed conventional and intelligent approaches in terms of performance and computational efficiency and …

based on reinforcement learning and analytical rolling models to guarantee sound product quality. Integrating an established … reinforcement learning algorithm Deep Deterministic Policy Gradient (DDPG) automatically proposes pass schedules by drawing upon …

regarding both sales and buying back resources further increases the problem's complexity. Reinforcement learning (RL …