… with reinforcement learning (RL) in the last century, and with the advent of deep learning (DL…
agents and autonomous systems, called deep reinforcement learning (DRL). In this paper, …

… controller via actor-critic reinforcement learning,” in Proc. 12th Int. Conf. … interactive
reinforcement learning,” in Proc. IEEE Int. Symp. … Ertel, “Reinforcement learning combined with …

… As an interdisciplinary area, the combination of reinforcement learning (RL) and neural … on
the state of the art neural network reinforcement learning (NNRL) algorithms. In addition to this…

… reinforcement learning-based framework for reliably inferring calibration parameters of battery
models in real … inferring the model parameters in real time with better accuracy compared …

… While RL is emerging as a practical component in reallife systems, most successes have
been in Single Agent domains. This report will instead specifically focus on challenges that are …

… Overview of Reinforcement Learning In this subsection, we provide a general overview of
the concepts of reinforcement learning. We aim to inform the reader of the fundamental …

… reinforcement learning. Journal of Machine Learning Research, 5(Nov): 1471–1530, 2004.
… Variance reduction for reinforcement learning in input-driven environments. Proceedings of …

… high level control methods that can learn and adapt themselves to changing conditions. …
is reinforcement learning. In this paper, a novel model-based reinforcement learning algorithm, …

… Deep reinforcement learning is the combination of reinforcement learning (RL) and deep
learning. This field of research has been able to solve a wide range of complex decisionmaking …

… how we will be converting a real-life scenario and observations into mathematical notation …
adequate coverage of all important scenarios in real life through our chosen formulation of the …