Reinforcement learning is an adaptive process in which an animal utilizes its … of reinforcement
learning play a central role in the newly emerging areas of neuroeconomics and decision …

… Reinforcement learning models in neuroscience face a challenge in accounting for learning
and decision making … import ideas from hierarchical reinforcement learning, a computational …

… learning about those preferences. In this document, we investigate ethical decision making
using the reinforcement-learning (RL) framework. We argue that reinforcement learning …

… We give an overview of recent exciting achievements of deep reinforcement learning (RL). …
We start with background of machine learning, deep learning and reinforcement learning. …

… problems could be viewed as sequential decision-making issues and is suitable to be …
of reinforcement learning and details of its application in the different domains (Figure 2). …

Many applications, such as robotics, are increasingly utilizing Reinforcement Learning (RL).
The current implementation of RL typically assumes that the state update from the previous …

… Because our primary goal is to develop a theoretical framework for understanding the
differential neural system contributions to decision making, we review studies with relevant neural …

… in studies of economic decision making has been to focus on “static” decision making (Edwards, …
on recent results aimed at elucidating the neural basis of model-based decision making. …

… -action value function Q(s, a) in reinforcement learning) was used for superposition state
updating. In … In the field of decision neuroscience, all reinforcement learning models involve the …

Robots that navigate among pedestrians use collision avoidance algorithms to enable safe
and efficient operation. Recent works present deep reinforcement learning as a framework to …