Titlebook: Recent Advances in Reinforcement Learning; Leslie Pack Kaelbling Book 1996 Springer Science+Business Media New York 1996 Performance.algor

直覺(jué)沒(méi)有

Thomas G. Dietterichof such an anomalous term and even to justify its existence. ., in his attempt to solve the problem, provided a rather questionable evaluation based on dubious analogies. We have attacked the problem directly and our calculations seem to confirm .’s assumption about the existence of a deep term (2.)

Vasodilation

MELD

Linear Least-Squares Algorithms for Temporal Difference Learning,TD algorithm depends linearly on σ.. In addition to converging more rapidly, LS TD and RLS TD do not have control parameters, such as a learning rate parameter, thus eliminating the possibility of achieving poor performance by an unlucky choice of parameters.

不適當(dāng)

Reinforcement Learning with Replacing Eligibility Traces,eas the method corresponding to replace-trace TD is unbiased. In addition, we show that the method corresponding to replacing traces is closely related to the maximum likelihood solution for these tasks, and that its mean squared error is always lower in the long run. Computational results confirm t

AVID

HARP

The Effect of Representation and Knowledge on Goal-Directed Exploration with Reinforcement-Learning of the topology of the state spaces. Our results provide guidance for empirical reinforcement-learning researchers on how to distinguish hard reinforcement-learning problems from easy ones and how to represent them in a way that allows them to be solved efficiently.

FIG

Creating Advice-Taking Reinforcement Learners,pected reward. A second experiment shows that advice improves the expected reward regardless of the stage of training at which it is given, while another study demonstrates that subsequent advice can result in further gains in reward. Finally, we present experimental results that indicate our method

limber

Book 1996eviewed original research comprising twelve invitedcontributions by leading researchers. This research work has also beenpublished as a special issue of .Machine Learning. (Volume 22,Numbers 1, 2 and 3).

動(dòng)作謎

e ofpeer-reviewed original research comprising twelve invitedcontributions by leading researchers. This research work has also beenpublished as a special issue of .Machine Learning. (Volume 22,Numbers 1, 2 and 3).978-1-4419-5160-1978-0-585-33656-5

歡樂(lè)中國(guó)

Book 1996Intelligence and Neural Networkcommunities. .Reinforcement learning has become a primary paradigm of machinelearning. It applies to problems in which an agent (such as a robot, aprocess controller, or an information-retrieval engine) has to learnhow to behave given only information about the success