… in reinforcement learning remain unsolved. One that draws more and more attention is the
issue of overfitting in reinforcement learning (Sutton… gap in deep reinforcement learning. They …

… We consider the exploration-exploitation dilemma in finite-horizon reinforcement learning
problems whose state-action space is endowed with a metric. We introduce Kernel-UCBVI, a …

… In addition, deep learning has stimulated the further development of many … reinforcement
learning, such as hierarchical reinforcement learning (HRL), multiagent reinforcement learning, …

… To make it more efficient in dealing with high dimensional sensory data, Deep Reinforcement
Learning (DRL) with continuous state and action spaces has been developed. The usual …

… analysis. At the core of the automatic adjustment is our application of a reinforcement learning
… to the current configuration and its associated analysis cost with respect to the user budget…

… are potentially useful in reinforcement learning. In particular, … We also conduct a full
mathematical analysis of the proposed … We empirically analyze the numerical properties of optimal …

… We study online reinforcement learning in average-reward stochastic games (SGs). An SG
models a two-player zero-sum game in a Markov environment, where state transitions and …

… Abstract—Reinforcement learning is a learning … reinforcement learning upon the fast
development of deep neural networks. Along with the promising prospects of reinforcement learning …

… 2 Single-agent learning This section presents the formalism of reinforcement learning and
its main components before outlining deep reinforcement learning along with its particular …

… Reinforcement Learning (RL) is learning what action to take next by mapping situations to
actions so as to maximize cumulative rewards. In recent years RL has achieved great success …