In recent years, reinforcement learning (RL) has attracted significant attention from both
industry and academia due to its success in solving some complex problems. This paper …

Reinforcement Learning (RL) algorithms have had tremendous success in simulated
domains. These algorithms, however, often cannot be directly applied to physical systems …

This article delves into the challenging supply chain management domain, explicitly
addressing the intricate issue of perishable inventory allocation within a two-echelon supply …

This paper investigates the economic operation of a microgrid with a variety of distributed
energy resources. Given the intermittency of renewable generation and the high …

Portfolio traders strive to identify dynamic portfolio allocation schemes that can allocate their
total budgets efficiently through the investment horizon. This study proposes a novel portfolio …

With the proliferation of mobile networks, we face strong diversification of services,
demanding the current network to embed more flexibility. To satisfy this daring need …

Abstract Many problems in Reinforcement Learning (RL) seek an optimal policy with large
discrete multidimensional yet unordered action spaces; these include problems in …

A unified distributed reinforcement learning (RL) solution is offered for both static and
dynamic economic dispatch problems (EDPs). Each agent is assigned with a fixed, discrete …

With the global trade competition becoming further intensified, Supply Chain Management
(SCM) technology has become critical to maintain competitive advantages for enterprises …

Portfolio management is important for sequential investment decisions in response to
fluctuating financial markets. As portfolio management can be formulated as a sequential …