In this article, we aim to provide a literature review of different formulations and approaches
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …

Improvement of traffic signal control (TSC) efficiency has been found to lead to improved
urban transportation and enhanced quality of life. Recently, the use of reinforcement …

Sequential decision making, commonly formalized as Markov Decision Process (MDP)
optimization, is an important challenge in artificial intelligence. Two key approaches to this …

Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has
become one of the most important and useful technology. It is a learning method where a …

Many real-world problems, such as network packet routing and urban traffic control, are
naturally modeled as multi-agent reinforcement learning (RL) problems. However, existing …

Ability to continuously learn and adapt from limited experience in nonstationary
environments is an important milestone on the path towards general intelligence. In this …

The key challenge in multiagent learning is learning a best response to the behaviour of
other agents, which may be non-stationary: if the other agents adapt their strategy as well …

Recent developments in deep reinforcement learning are concerned with creating decision-
making agents which can perform well in various complex domains. A particular approach …

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