Reinforcement learning (RL) algorithms find applications in inventory control, recommender
systems, vehicular traffic management, cloud computing, and robotics. The real-world …

Energy consumption for heating, ventilation and air conditioning (HVAC) has increased
significantly and accounted for a large proportion of building energy growth. Advanced …

Reinforcement learning (RL) methods learn optimal decisions in the presence of a stationary
environment. However, the stationary assumption on the environment is very restrictive. In …

Large non-residential buildings can contain complex and often inefficient water distribution
systems. As requirements for water increase due to water scarcity and industrialization, it …

Deep reinforcement learning (DRL) is a powerful tool for learning from interactions within a
stationary environment where state transition and reward distributions remain constant …

Manufacturing process monitoring is crucial to ensure production quality. This paper
formulates the detection problem of abnormal changes in the manufacturing process as the …

Detecting relevant changes in dynamic time series data in a timely manner is crucially
important for many data analysis tasks in real-world settings. Change point detection …

Reinforcement learning (RL) algorithms have been widely applied in solving traffic signal
control problems. Traffic environments, however, are intrinsically nonstationary, which …

Reinforcement Learning (RL) within non-stationary environments presents a formidable
challenge. In some applications, anticipating abrupt alterations in the environment model …

In lifelong learning an agent learns throughout its entire life without resets, in a constantly
changing environment, as we humans do. Consequently, lifelong learning comes with a …