… and reinforcement learning theories of decision making. (a) In economic theories, decision
making … (b) In reinforcement learning, actions are chosen probabilistically (ie, softmax) on the …

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

… 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 …

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

… This paper surveys the field of reinforcement learning from a … learning. Both the historical
basis of the field and a broad selection of current work are summarized. Reinforcement learning …

… We will first consider decision making when the rules are given and then move onto the
standard reinforcement learning problem in which the rules of the task are unknown and the …

… reinforcement learning approach with value function approximation and feature learning is
proposed for autonomous decision making … , the sequential decision making problem for lane …

… history of both players in making its predictions. These … reinforcement learning, suggesting
that the animals sought optimal decision-making strategies using reinforcement learning …

… While such decisions are typically made heuristically, we bring to … reinforcement learning
to learn decisionmaking strategies in a data-driven way. Our framework allows trackers to learn …

… -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 …