… This paper aims to demonstrate how a deep reinforcement learning agent based on the
proximal … The deep reinforcement learning agent is built upon the Proximal Policy Optimization …

… that fail to model the complicated supply-dynamics and restrict the performance … supply-demand-aware
deep reinforcement learning algorithm for taxi dispatching, where we use a deep …

… CONCLUSION AND FUTURE WORKS In this paper, a hierarchical mixed deep reinforcement
learning method is proposed to balance the vehicle supply and demand in the city. First, a …

… In this paper, two Deep Reinforcement Learning (DRL) based methods are proposed to
solve multi-period capacitated supply chain optimization problem under demand uncertainty. …

… Deep Reinforcement Learning approach to operate a multi-echelon supply chain with uncertain
demands… The present work uses a Deep Reinforcement Learning approach, namely the …

… framework based on deep reinforcement learning (RL) for dynamically selecting the optimal
demand forecast model for each of the products with the corresponding demand patterns to …

… demand during peak period through rewards. In this study, an incentive-based DR program
with modified deep learning and reinforcement learning is proposed. A modified deep …

… We investigate the recent works using deep reinforcement learning (DRL) techniques to
solve DDS problems in the two stages. We also discuss the further challenges and open …

… In this paper, we study the performance of various deep reinforcement learning algorithms
… , direct demand control signals, and electricity prices. Seven deep reinforcement learning …

… , the interactions of supply and demand sides of integrated energy … deep reinforcement
learning (DRL) algorithm for optimal scheduling of IES. Firstly, two supply-side and three demand-…