… In this paper we proposed Q-learning algorithm and evaluation of RL techniques (Reinforcement
learning architecture, … Learning agent, the fundamental element of reinforcement …

In this paper we present a new way of predicting the performance of a reinforcement learning
policy given historical data that may have been generated by a different policy. The ability …

… Further interrogation of the state-action value function will be made later when I cover multiple
approaches to evaluating reinforcement-learning algorithms with batch data. …

We study the problem of off-policy value evaluation in reinforcement learning (RL), where
one aims to estimate the value of a new policy based on data collected by a different policy. …

… DRL methods on select MDP instances, evaluating the … evaluating on an MDP family is
nontrivial. Overall, this work identifies new challenges for empirical rigor in reinforcement learning…

… this section we analyze some of the evaluation metrics commonly used in the reinforcement
… We focus on evaluation methods for the policy optimization view (with offline evaluation), but …

… Reinforcement learning (RL) is one of the most vibrant research frontiers in machine
learning … In this paper, we primarily focus on off-policy evaluation (OPE), one of the most …

… in evaluation in RL compared to supervised learning, … evaluation in RL, and propose an
evaluation pipeline that can be decoupled from the algorithm code. We hope such an evaluation …

… empirical evaluation, to act as a foundation for future comparative studies. Two classes of
multiobjective reinforcement learning algorithms are identified, and appropriate evaluation …

… learning and deep reinforcement learning efforts [23, 35]. As OPE is central to real-world
applications of reinforcement learning… work on OPE evaluation for reinforcement learning [17, 18…