… We consider the standard reinforcement learning formalism consisting of an agent
interacting with an environment. To simplify the exposition we assume in this section that the …

We present a user study to investigate the impact of explanations on non-experts' understanding
of reinforcement learning (RL) agents. We investigate both a common RL visualization, …

… the role that reinforcement learning can play in the optimization of treatment policies for
chronic illnesses. Before applying any off-the-shelf reinforcement learning methods in this setting…

… and empirical study for off-policy policy evaluation (OPE) in reinforcement learning, which is
a … Given the increasing interest in deploying learning-based methods, there has been a flurry …

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

… In this paper, we perform an empirical study of several information encoding architectures
using data from septic patients in the MIMIC-III dataset to form representations of a patient state…

… critical for an agent to rapidly adapt to different environments and effectively learn new tasks.
In this paper we conduct an empirical study of Deep Q-Networks (DQNs) where the agent is …

… To fill this gap, in this paper, we conduct a large-scale empirical study of 927 DRL-related
posts extracted from Stack Overflow, the most popular Q &A platform in the software community…

… Abstract: In this paper a reinforcement learning methodology for automatic online algorithm
selection is introduced and empirically tested. It is applicable to automatic algorithm selection …

… finite reward to absorbing goal states This paper also presents a detailed empirical study
of Rilearning, an average reward rt-infarcenitvnt learning method, using two empiiacal l1l'.‘5I[…