Computational models of reinforcement learning have played an important role in
understanding learning and decision making behavior, as well as the neural mechanisms …

Background Reinforcement learning models provide excellent descriptions of learning in
multiple species across a variety of tasks. Many researchers are interested in relating …

We examine the question of when and how parametric models are most useful in
reinforcement learning. In particular, we look at commonalities and differences between …

Highlights•'Reinforcement learning'(RL) refers to different concepts in machine learning,
psychology, and neuroscience.•In psychology and neuroscience, RL models have provided …

Abstract Machine Learning is a field of research aimed at constructing intelligent machines
that gain and improve their skills by learning and adaptation. As such, Machine Learning …

Abstract Recently, Bayesian Optimization (BO) has been used to successfully optimize
parametric policies in several challenging Reinforcement Learning (RL) applications. BO is …

Abstract We introduce ASPiRe (Adaptive Skill Prior for RL), a new approach that leverages
prior experience to accelerate reinforcement learning. Unlike existing methods that learn a …

In reinforcement learning (RL), a decision maker searching for the most rewarding option is
often faced with the question: What is the value of an option that has never been tried …

A key question in reinforcement learning is how an intelligent agent can generalize
knowledge across different inputs. By generalizing across different inputs, information …

Reinforcement learning (RL) models have been broadly used in modeling the choice
behavior of humans and other animals. In standard RL models, the action values are …