… In many applications of reinforcement learning, actively gathering data through interactions
… Offline (or batch) reinforcement learning (RL) avoids this problem by learning a policy solely …

… a class of reinforcement learning problems where the reward signals for policy learning are
… To this end, in the following subsections, we theoretically and empirically analyze Eq. (13) …

… Abstract—Offline reinforcement learning (RL) aims at learning an optimal policy from a
static offline data set, without interacting with the environment. However, the theoretical …

… learning, we employ Q-learning as the base of the reinforcement learning algorithm, apply
the sorting … reinforcement learning will be similar when they both reach a similar training level. …

… In this section we analyze some of the evaluation metrics commonly used in the reinforcement
learning literature. In practice, RL algorithms are often evaluated by simply presenting …

… analyze the effect of the activation function and the kernel initializer on the performance of
deep reinforcement learning… Empirical analyses are shown in Section III. Section IV concludes …

… Our analysis accommodates a variety of policy value … difference learning estimators, and
double reinforcement learning … design strategies for policy evaluation in reinforcement learning. …

… where r(s, a, s ) is the original reward and r (s, a, s ) is the transformed reward, so ideally
we could solve (2) by applying any reinforcement learning algorithm with these reshaped …

… Reinforcement learning (RL) is a framework of particular importance to psychology,
neuroscience and machine learning. Interactions between these fields, as promoted through the …

… In our first empirical analysis, we only changed the maximum negative reward per episode
(MaxNegR) parameter to -10, -100, -5000, and -100 000. We selected these values to …