… Reinforcement Learning “This book provides an accessible introduction to deep reinforcement
learning … resource for anyone looking to apply deep reinforcement learning in practice.” …

… Enhancing transfer in reinforcement learning by building stochastic models of robot actions.
In Machine Learning: Pr·oceedings of the Ninth Inter·nat'tonal Workshop. Morgan Kaufmnn, …

… This paper surveys the field of reinforcement learning from a … learning. Both the historical
basis of the field and a broad selection of current work are summarized. Reinforcement learning …

… implemented via a reinforcement learning (RL) … the theory of fair reinforcement learning,
organizing them into single-agent RL, multi-agent RL, long-term fairness via RL, and offline learn…

… This article is a gentle discussion about the field of reinforcement learning in practice, about
… to reinforcement learning (RL), and its relationship with deep learning, machine learning …

… In the next section, we formulate reinforcement learning … value function methods for
reinforcement learning, which estimate … In this section, we define the reinforcement learning …

… REINFORCEMENT LEARNING This section gives a non-exhaustive overview of recent
developments related to Reinforcement Learning (RL) and demand response. Perhaps the most …

… From fundamental underlying theory of deep reinforcement learning to technical
implementation with elaborated code details, the authors devoted significant efforts to provide a …

… Reinforcement Learning (RL) has achieved tremendous … from the perspectives of methods,
theory, and applications. Firstly… Secondly, we analyze the theory and algorithm progress from …

… Lately, due to advances in theory about the brain, and especially because testing and
measuring of brain activity (fMRI, EEG, etc.) has become much better, much research tries to either …