Novel advanced policy gradient (APG) methods, such as trust region policy optimization and
proximal policy optimization (PPO), have become the dominant reinforcement learning …

In order to represent the semantics and concepts better, the ontology, as an efficient model,
has penetrated into all research areas of the computer science and information technology …

Within the combinatorial scheduling community, there has been an increasing interest in
modelling and solving scheduling problems in dynamic environments. Such problems have …

The real option management of commodity conversion assets gives rise to intractable
Markov decision processes (MDPs), in part because of the use of high-dimensional models …

Summary Hybrid Ad‐hoc NETwork (HANET) is a fusion of both the static and dynamic
topologies. Each node of this network consists of low capacity battery. Because of …

Admission decisions for loss systems accessed by multiple customer classes are a classical
queueing control problem with a wide variety of applications. When a server is available, the …

With the rapid advance of information technology, network systems have become
increasingly complex and hence the underlying system dynamics are often unknown or …

We consider the linear programming approach to approximate dynamic programming with
an average cost objective and a finite state space. Using a Lagrangian form of the linear …

Queueing systems appear in many important real-life applications including communication
networks, transportation and manufacturing systems. Reinforcement learning (RL) …

The continuing growth of the older population has led to a significant increase in demand for
rehabilitation over the past two decades and subsequently in long delays in admission to …