The vehicle routing problem with time windows (VRPTW) as one of the most known
combinatorial operations (CO) problem is considered to be a tough issue in practice and the …

In multi-task learning, there are roughly two approaches to discovering representations. The
first is to discover task relevant representations, ie, those that compactly represent solutions …

The booming edge computing market that is supported by the edge cloud (EC) infrastructure
has brought huge operating costs, mainly the energy cost, to edge service providers. The …

Gradient-based methods are often used for policy optimization in deep reinforcement
learning, despite being vulnerable to local optima and saddle points. Although gradient-free …

Off-Policy Shaping Ensembles in Reinforcement Learning Page 1 Off-Policy Shaping
Ensembles in Reinforcement Learning Anna Harutyunyan and Tim Brys and Peter Vrancx and …

Reinforcement learning (RL) agents naturally struggle with longsequence sparse reward
tasks due to the lack of reward feedback during exploration and the problem of identifying …

Multi-agent systems (MASs) are a form of distributed intelligence, where multiple
autonomous agents act in a common environment. Numerous complex, real world systems …

A significant challenge in reinforcement learning is quantifying the complex relationship
between actions and long-term rewards. The effects may manifest themselves over a long …

Energy consumption in data center is currently the main focus of many large-scale
enterprises and cloud service providers. Dynamic virtual machine (VM) consolidation …

Reinforcement learning is a robust artificial intelligence solution for agents required to act in
an environment, making their own decisions on how to behave. Typically an agent is …