… decision making when the rules are given and then move onto the standard reinforcement
learning … and the subject must discover how best to behave by trial and error. Given the rules, …

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

… Participants in this study were tested in a sequential reinforcement-based task. Modeling the
participants’ behavior by a decision tree, it was found that their behavior is consistent with a …

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

… learning tasks. If neural prediction error signals are used to guide decision-making, as
suggested by reinforcement learning … to decision outcomes should relate to subsequent decision …

… to decision making is not enough and the existing decision … the decision-making training
and learning is put forward in this paper. It consists of two parts: the deep reinforcement learning …

… We introduce a framework that abstracts Reinforcement Learning (… In particular, we present
Decision Transformer, an … compute policy gradients, Decision Transformer simply outputs the …

… Markov decision processes and two classes of algorithms for computing optimal behaviors:
reinforcement learning and … First the formal framework of Markov decision process is defined, …

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

… The above evidence suggests that the BG and OFC intimately interact in reinforcement
learning and decision making. Given that medial OFC areas tend to be activated by positive …