… I propose using reinforcement learning to analyze the regret … I have developed a new library
of reinforcement learning tools, … Finally, I use our new reinforcement learning tools to provide …

… We empirically analyze the properties of these new emphatic algorithms to observe how
qualitative properties such as convergence, learning speed and variance manifest in practice. …

… For our purposes, the final outcome resulting from a reinforcementlearning algorithm is a
policy. … reinforcement learning from two different levels. First, defining reinforcement learning as …

… More specifically, reinforcement learning methods, including multiple regressions, random …
crucial aspect of machine learning models. In addition, reinforcement learning is analogous to …

… evaluation for the analysis, but we will compare both approaches empirically. Now, we …
the broader impact of our contribution to be the same as the one of reinforcement learning. …

… Then, we will discuss how reinforcement learning fits into this picture and discuss the …
discuss how reinforcement learning fits in the larger research fields of machine learning and …

… To identify the effect of overfitting in independent reinforcement learners, we introduce joint
policy correlation (JPC) matrices. To simplify the presentation, we describe here the special …

… In this project we present a Reinforcement Learning (RL) approach for inducing effective
pedagogical strategies and empirical evaluations of the induced strategies. This paper …

… Reinforcement learning (RL) formalises the problems of evaluation and optimisation of
an … We refer to these approaches as categorical distributional reinforcement learning (CDRL). …

… This work mostly looked at a theoretical foundation of reinforcement learning, which, in
the author’s view, contributes to understand RL as a general and natural phenomenon of the …