Adolescents are notorious for engaging in reward-seeking behaviors, a tendency attributed to heightened activity in the brain's reward systems during adolescence. It has been suggested that reward sensitivity in adolescence might be adaptive, but evidence of an adaptive role has been scarce. Using a probabilistic reinforcement learning task combined with reinforcement learning models and fMRI, ...

Consider learning a policy from example expert behavior, without interaction with the expert or access to reinforcement signal. One approach is to recover the expert’s cost function with inverse reinforcement learning, then extract a policy from that cost function with reinforcement learning. This approach is indirect and can be slow. We propose a new general framework for directly extracting a...

In this paper we present a hybrid system combining techniques from symbolic planning and reinforcement learning. Planning is used to automatically construct task hierarchies for hierarchical reinforcement learning based on abstract models of the behaviours’ purpose, and to perform intelligent termination improvement when an executing behaviour is no longer appropriate. Reinforcement learning is...

Research on human and animal behavior has long emphasized its hierarchical structure-the divisibility of ongoing behavior into discrete tasks, which are comprised of subtask sequences, which in turn are built of simple actions. The hierarchical structure of behavior has also been of enduring interest within neuroscience, where it has been widely considered to reflect prefrontal cortical functio...

In this work, we propose a framework of learning with preferences, which combines some neurophysiological findings, prospect theory, and the classic reinforcement learning mechanism. Specifically, we extend the state representation of reinforcement learning with a multi-dimensional preference model controlled by an external state. This external state is designed to be independent from the reinf...

This paper describes a formulation of reinforcement learning that enables learning in noisy, dynamic environments such as in the complex concurrent multi-robot learning domain. The methodology involves minimizing the learning space through the use of behaviors and conditions, and dealing with the credit assignment problem through shaped reinforcement in the form of heterogeneous reinforcement f...

learning, neural networks, robotics, AI, and engineering. In recognition of the growing importance of reinforcement learning, it seemed an opportune time to bring together leading researchers from these areas for a three-day meeting consisting of general and wide-ranging discussions. The National Science Foundation (NSF) sponsored the workshop with a generous grant to cover the travel and lodgi...

Based on the previously proposed extended neural-fuzzy network, this paper presents a cooperation scheme of training data based learning and reinforcement learning for constructing sensor-based behaviour modules in robot navigation. In order to solve reinforcement learning problem, a reinforcement-based neural-fuzzy control system (RNFCS) is provided, which consists of a neural-fuzzy controller...

A general technique is proposed for embedding online clustering algorithms based on competitive learning in a reinforcement learning framework. The basic idea is that the clustering system can be viewed as a reinforcement learning system that learns through reinforcements to follow the clustering strategy we wish to implement. In this sense, the reinforcement guided competitive learning (RGCL) ...

ion and Generalization in Reinforcement Learning: A Summary and Framework Marc Ponsen, Matthew E. Taylor, and Karl Tuyls 1 Universiteit Maastricht, Maastricht, The Netherlands {m.ponsen,k.tuyls}@maastrichtuniversity.nl 2 The University of Southern California, Los Angeles, CA [email protected] Abstract. In this paper we survey the basics of reinforcement learning, generalization and abstraction. W...