With the rapid development of the big data era, deep reinforcement learning has gradually
become a research hotspot in artificial intelligence. This study focuses on the creative use of …

The evaluation of a hypermedia learning environment: the CESAR experience: Journal of
Educational Multimedia and Hypermedia: Vol 5, No 1 skip to main content ACM Digital Library …

Create a smart classroom that uses many sensors, such as webcams, to monitor each
student's progress and leverages deep reinforcement learning (DRL) algorithms to provide …

In modern ages, the study on Reinforcement Learning (RL) has driven on Deep Q-Network
(DQN) optimization learning prediction and control of Markov decision processes (MDPs). In …

The Reinforcement Learning (RL) implement real-time decision-making behavior in
uncertain environment in order to optimize overall target. RL is considered to be the …

Among the previous studies on deep reinforcement learning (DRL) challenges, optimizing
data usage efficiency in rich and complex environments has been discussed by many …

In order to overcome the limitation of traditional reinforcement learning techniques on the
restricted dimensionality of state and action spaces, the recent breakthroughs of deep …

Reinforcement learning is a model structure that learns and updates the model based on
receiving the reward and feedback of certain environments. In recent researches …

Training game intelligence through reinforcement learning (RL) and applying games to
teaching and learning can stimulate students' enthusiasm for learning. Therefore, the study …

DQN (Deep Q-Network) is a method to perform Q-learning for reinforcement learning using
deep neural networks. DQNS require a large buffer and batch processing for an experience …