نتایج جستجو برای: keywords reinforcement learning

تعداد نتایج: 2453256  

Journal: :I. J. Humanoid Robotics 2015
Rok Vuga Bojan Nemec Ales Ude

In this paper, we propose an integrated policy learning framework that fuses iterative learning control (ILC) and reinforcement learning. Integration is accomplished at the exploration level of the reinforcement learning algorithm. The proposed algorithm combines fast convergence properties of iterative learning control and robustness of reinforcement learning. This way, the advantages of both ...

Journal: :IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society 2000
Chin-Teng Lin Chong-Ping Jou

This paper proposes a TD (temporal difference) and GA (genetic algorithm)-based reinforcement (TDGAR) learning method and applies it to the control of a real magnetic bearing system. The TDGAR learning scheme is a new hybrid GA, which integrates the TD prediction method and the GA to perform the reinforcement learning task. The TDGAR learning system is composed of two integrated feedforward net...

Journal: :Journal of vision 2006
Aaron R Seitz Jose E Nanez Steve Holloway Yoshiaki Tsushima Takeo Watanabe

The role of external reinforcement is an issue of much debate and uncertainty in perceptual learning research. Although it is commonly acknowledged that external reinforcement, such as performance feedback, can aid in perceptual learning (M. H. Herzog & M. Fahle, 1997), there are many examples in which it is not required (K. Ball & R. Sekuler, 1987; M. Fahle, S. Edelman, & T. Poggio, 1995; A. K...

Journal: :Neuron 2016
Juliet Y. Davidow Karin Foerde Adriana Galván Daphna Shohamy

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, ...

2016
Jonathan Ho Stefano Ermon

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...

2002
Malcolm R. K. Ryan

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...

Journal: :Cognition 2009
Matthew M Botvinick Yael Niv Andrew C Barto

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...

2015
Johannes Feldmaier Hao Shen

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...

Journal: :Victorian Literature and Culture 2018

Journal: :Auton. Robots 1997
Maja J. Mataric

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...

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