Turing’s ideas ultimately led to the development of reinforcement learning, a branch of artificial intelligence. Reinforcement learning designs intelligent agents by training them to maximize ...

Reinforcement learning involves ... and TensorFlow” by Aurélien Géron, and “Python Machine Learning” by Sebastian Raschka. These books cover the fundamentals of deep learning and provide ...

Abstract: This review article addresses the problem of learning abstract representations of measurement data in the context of deep reinforcement learning. While the data are often ambiguous, ...

In his new book, “Dangerous Learning: The South’s Long War on Black Literacy,” legal scholar Derek Black tells the story of the resistance to Black literacy in America. The author looks at ...

Making decisions is a critical aspect of human behavior. Reinforcement learning has been investigated in decision-making experiments with the goal of deciphering learning and improve our understanding ...

We propose a novel method that applies aggregation based on the local data sizes for the shared knowledge layers and uses a deep reinforcement learning (DRL) agent for aggregating the layers ...

MPC, a well-known control methodology that exploits a prediction model to predict the future behaviour of the environment and compute the optimal action and RL, a Machine Learning paradigm that showed ...

Reinforcement Learning - An Introduction ... Although the authors have made the book extremely clear and friendly to readers at each level, this book is honestly still intimidating to RL or ML ...

Recent investigation on reinforcement learning (RL) has demonstrated considerable flexibility in dealing with various problems. However, such models often experience difficulty learning seemingly easy ...