Balancing traffic among cellular networks is very challenging due to many factors.
Nevertheless, the explosive growth of mobile data traffic necessitates addressing this …

This paper synchronizes control theory with computer vision by formalizing object tracking
as a sequential decision-making process. A reinforcement learning (RL) agent successfully …

In this paper, we propose a reinforcement learning (RL) framework that uses a multimodal
advantage function (MAF) to come close to the true advantage function, thereby achieving …

The microgrid is a solution for integrating renewable energy resources into the power
system. However, overcoming the randomness of these nature-based resources requires a …

Geometry optimization is a crucial step in computational chemistry, and the efficiency of
optimization algorithms plays a pivotal role in reducing computational costs. In this study, we …

The accurate estimation of Q-function and the enhancement of agent's exploration ability
have always been challenges of off-policy actor–critic algorithms. To address the two …

As an auxiliary loss function, the mutual information constraint is widely used in various
deep learning tasks, such as deep reinforcement learning and representation learning …

Maximum entropy deep reinforcement learning has shown great promise in tackling various
challenging continuous tasks. By incorporating the maximum entropy framework, the goal is …

The use of the AWS environment with DeepRacer provides an interesting educational
learning platform, which allows to train and apply machine learning models in vehicles that …

Deep reinforcement learning in maximum entropy framework is sample-efficient and has a
strong exploration capacity, making it effective and favorable to solve problems like path …