… For each of these different reinforcement learning scenarios, … , with an emphasis on
understanding the statistical properties of the … provoking statistical treatment of reinforcement …

… decision making by a learning agent. In this chapter, we review the necessary concepts
of reinforcement learning, connect them to the relevant statistical literature, and develop a …

… Reinforcement learning with function approximation has … under which efficient reinforcement
learning is possible. From … emerged for sample efficient reinforcement learning: MDPs with …

… We consider the setting in which a reinforcement learning algorithm is evaluated on M
tasks. For each of these tasks, we perform N independent runs3 which each provide a scalar, …

… reinforcement learning algorithms. After introducing the concepts of statistical testing, we
review the relevant statistical … in terms of false positive rate and statistical power as a function of …

… reinforcement learning (DRL) algorithms in terms of recursively estimating statistics of the …
statistical estimator and a method for imputing a return distribution consistent with that set of …

… in learning and decision making are internally represented. To articulate the significance of
representation for reinforcement learning we … into the statistical structure of naturalistic tasks. …

… reinforcement learning to further potential fields of manufacturing. So, the current paper
introduces novel RL based, Statistical … is the adaptation concept of reinforcement learning to the …

… In this paper we will be discussing Statistical Approach in detail… a descriptive analysis
Reinforcement learning and its future … STATISTICAL LEARNING APPROACH In statistical …

… the learning of words and structural rules. In lack of reliable online measures, statistical word
and rule learning … gap between language learning and reinforcement learning phenomena. …