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363
Combining Online and Offline Knowledge in UCT
 In Zoubin Ghahramani, editor, Proceedings of the International Conference of Machine Learning (ICML 2007
, 2007
"... The UCT algorithm learns a value function online using samplebased search. The TD(λ) algorithm can learn a value function offline for the onpolicy distribution. We consider three approaches for combining offline and online value functions in the UCT algorithm. First, the offline value function is ..."
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Cited by 78 (4 self)
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The UCT algorithm learns a value function online using samplebased search. The TD(λ) algorithm can learn a value function offline for the onpolicy distribution. We consider three approaches for combining offline and online value functions in the UCT algorithm. First, the offline value function is used as a default policy during MonteCarlo simulation. Second, the UCT value function is combined with a rapid online estimate of action values. Third, the offline value function is used as prior knowledge in the UCT search tree. We evaluate these algorithms in 9 × 9 Go against GnuGo 3.7.10. The first algorithm performs better than UCT with a random simulation policy, but surprisingly, worse than UCT with a weaker, handcrafted simulation policy. The second algorithm outperforms UCT altogether. The third algorithm outperforms UCT with handcrafted prior knowledge. We combine these algorithms in MoGo, the world’s strongest 9 × 9 Go program. Each technique significantly improves MoGo’s playing strength. 1.
Evolutionary function approximation for reinforcement learning
 Journal of Machine Learning Research
, 2006
"... Ø�ÓÒ�ÔÔÖÓÜ�Ñ�Ø�ÓÒ�ÒÓÚ�Ð�ÔÔÖÓ��ØÓ�ÙØÓÑ�Ø��ÐÐÝ× � Ø�ÓÒ�Ð���×�ÓÒ×Ì��×Ø��×�×�ÒÚ�×Ø���Ø�×�ÚÓÐÙØ�ÓÒ�ÖÝ�ÙÒ �Ò�ÓÖ�Ñ�ÒØÐ��ÖÒ�Ò�ÔÖÓ�Ð�Ñ×�Ö�Ø��×Ù�×�ØÓ�Ø��×�Ø�×� × ÁÒÑ�ÒÝÑ���Ò�Ð��ÖÒ�Ò�ÔÖÓ�Ð�Ñ×�Ò���ÒØÑÙ×ØÐ��ÖÒ Ñ�ÒØ���Ò×Ø�ÒØ��Ø�ÓÒÓ��ÚÓÐÙØ�ÓÒ�ÖÝ�ÙÒØ�ÓÒ�ÔÔÖÓÜ�Ñ � Ù�Ð×Ø��Ø�Ö���ØØ�Ö��Ð�ØÓÐ��ÖÒÁÔÖ�×�ÒØ��ÙÐÐÝ�ÑÔÐ � Ø�Ó ..."
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Cited by 72 (15 self)
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Ø�ÓÒ�ÔÔÖÓÜ�Ñ�Ø�ÓÒ�ÒÓÚ�Ð�ÔÔÖÓ��ØÓ�ÙØÓÑ�Ø��ÐÐÝ× � Ø�ÓÒ�Ð���×�ÓÒ×Ì��×Ø��×�×�ÒÚ�×Ø���Ø�×�ÚÓÐÙØ�ÓÒ�ÖÝ�ÙÒ �Ò�ÓÖ�Ñ�ÒØÐ��ÖÒ�Ò�ÔÖÓ�Ð�Ñ×�Ö�Ø��×Ù�×�ØÓ�Ø��×�Ø�×� × ÁÒÑ�ÒÝÑ���Ò�Ð��ÖÒ�Ò�ÔÖÓ�Ð�Ñ×�Ò���ÒØÑÙ×ØÐ��ÖÒ Ñ�ÒØ���Ò×Ø�ÒØ��Ø�ÓÒÓ��ÚÓÐÙØ�ÓÒ�ÖÝ�ÙÒØ�ÓÒ�ÔÔÖÓÜ�Ñ � Ù�Ð×Ø��Ø�Ö���ØØ�Ö��Ð�ØÓÐ��ÖÒÁÔÖ�×�ÒØ��ÙÐÐÝ�ÑÔÐ � Ø�ÓÒÛ���ÓÑ��Ò�×Æ��Ì�Ò�ÙÖÓ�ÚÓÐÙØ�ÓÒ�ÖÝÓÔØ�Ñ�Þ � Ð�Ø�Ò��ÙÒØ�ÓÒ�ÔÔÖÓÜ�Ñ�ØÓÖÖ�ÔÖ�×�ÒØ�Ø�ÓÒ×Ø��Ø�Ò��Ð� Ø�ÓÒØ��Ò�ÕÙ�Û�Ø�ÉÐ��ÖÒ�Ò��ÔÓÔÙÐ�ÖÌ�Ñ�Ø�Ó�Ì� � �Æ��ÒØ�Ò��Ú��Ù�ÐÐ��ÖÒ�Ò�Ì��×Ñ�Ø�Ó��ÚÓÐÚ�×�Ò��Ú� � ÓÔØ�Ñ�Þ�Ø�ÓÒ��ÐÐ�ÒØ��×�Ø��ÓÖÝ��Ú�ÐÓÔ�Ò��«�Ø�Ú�Ö��Ò �ÓÖÁÒ×Ø����ØÖ���Ú�×ÓÒÐÝÔÓ×�Ø�Ú��Ò�Ò���Ø�Ú�Ö�Û�Ö� × ÔÖÓ�Ð�Ñ××Ù��×ÖÓ�ÓØÓÒØÖÓÐ��Ñ�ÔÐ�Ý�Ò��Ò�×Ý×Ø�Ñ �ÒÛ���Ø�����ÒØÒ�Ú�Ö×��×�Ü�ÑÔÐ�×Ó�ÓÖÖ�Ø����Ú 1.
Nearly tight bounds for the continuumarmed bandit problem
 Advances in Neural Information Processing Systems 17
, 2005
"... In the multiarmed bandit problem, an online algorithm must choose from a set of strategies in a sequence of n trials so as to minimize the total cost of the chosen strategies. While nearly tight upper and lower bounds are known in the case when the strategy set is finite, much less is known when th ..."
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Cited by 69 (4 self)
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In the multiarmed bandit problem, an online algorithm must choose from a set of strategies in a sequence of n trials so as to minimize the total cost of the chosen strategies. While nearly tight upper and lower bounds are known in the case when the strategy set is finite, much less is known when there is an infinite strategy set. Here we consider the case when the set of strategies is a subset of R d, and the cost functions are continuous. In the d = 1 case, we improve on the bestknown upper and lower bounds, closing the gap to a sublogarithmic factor. We also consider the case where d> 1 and the cost functions are convex, adapting a recent online convex optimization algorithm of Zinkevich to the sparser feedback model of the multiarmed bandit problem. 1
A ContextualBandit Approach to Personalized News Article Recommendation
"... Personalized web services strive to adapt their services (advertisements, news articles, etc.) to individual users by making use of both content and user information. Despite a few recent advances, this problem remains challenging for at least two reasons. First, web service is featured with dynamic ..."
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Cited by 59 (11 self)
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Personalized web services strive to adapt their services (advertisements, news articles, etc.) to individual users by making use of both content and user information. Despite a few recent advances, this problem remains challenging for at least two reasons. First, web service is featured with dynamically changing pools of content, rendering traditional collaborative filtering methods inapplicable. Second, the scale of most web services of practical interest calls for solutions that are both fast in learning and computation. In this work, we model personalized recommendation of news articles as a contextual bandit problem, a principled approach in which a learning algorithm sequentially selects articles to serve users based on contextual information about the users and articles, while simultaneously adapting its articleselection strategy based on userclick feedback to maximize total user clicks. The contributions of this work are threefold. First, we propose a new, general contextual bandit algorithm that is computationally efficient and well motivated from learning theory. Second, we argue that any bandit algorithm can be reliably evaluated offline using previously recorded random traffic. Finally, using this offline evaluation method, we successfully applied our new algorithm to a Yahoo! Front Page Today Module dataset containing over 33 million events. Results showed a 12.5 % click lift compared to a standard contextfree bandit algorithm, and the advantage becomes even greater when data gets more scarce.
Learning diverse rankings with multiarmed bandits
 In Proceedings of the 25 th ICML
, 2008
"... Algorithms for learning to rank Web documents usually assume a document’s relevance is independent of other documents. This leads to learned ranking functions that produce rankings with redundant results. In contrast, user studies have shown that diversity at high ranks is often preferred. We presen ..."
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Cited by 56 (4 self)
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Algorithms for learning to rank Web documents usually assume a document’s relevance is independent of other documents. This leads to learned ranking functions that produce rankings with redundant results. In contrast, user studies have shown that diversity at high ranks is often preferred. We present two online learning algorithms that directly learn a diverse ranking of documents based on users ’ clicking behavior. We show that these algorithms minimize abandonment, or alternatively, maximize the probability that a relevant document is found in the top k positions of a ranking. Moreover, one of our algorithms asymptotically achieves optimal worstcase performance even if users’ interests change. 1.
Stochastic linear optimization under bandit feedback
 In submission
, 2008
"... In the classical stochastic karmed bandit problem, in each of a sequence of T rounds, a decision maker chooses one of k arms and incurs a cost chosen from an unknown distribution associated with that arm. The goal is to minimize regret, defined as the difference between the cost incurred by the alg ..."
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Cited by 47 (8 self)
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In the classical stochastic karmed bandit problem, in each of a sequence of T rounds, a decision maker chooses one of k arms and incurs a cost chosen from an unknown distribution associated with that arm. The goal is to minimize regret, defined as the difference between the cost incurred by the algorithm and the optimal cost. In the linear optimization version of this problem (first considered by Auer [2002]), we view the arms as vectors in Rn, and require that the costs be linear functions of the chosen vector. As before, it is assumed that the cost functions are sampled independently from an unknown distribution. In this setting, the goal is to find algorithms whose running time and regret behave well as functions of the number of rounds T and the dimensionality n (rather than the number of arms, k, which may be exponential in n or even infinite). We give a nearly complete characterization of this problem in terms of both upper and lower bounds for the regret. In certain special cases (such as when the decision region is a polytope), the regret is polylog(T). In general though, the optimal regret is Θ ∗ ( √ T) — our lower bounds rule out the possibility of obtaining polylog(T) rates in general. We present two variants of an algorithm based on the idea of “upper confidence bounds. ” The first, due to Auer [2002], but not fully analyzed, obtains regret whose dependence on n and T are both essentially optimal, but which may be computationally intractable when the decision set is a polytope. The second version can be efficiently implemented when the decision set is a polytope (given as an intersection √ of halfspaces), but gives up a factor of n in the regret bound. Our results also extend to the setting where the set of allowed decisions may change over time.
Bandit Algorithm for Tree Search
, 2007
"... apport de recherche ISSN 02496399 ISRN INRIA/RR6141FR+ENG ..."
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Cited by 45 (15 self)
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apport de recherche ISSN 02496399 ISRN INRIA/RR6141FR+ENG
MultiArmed Bandits in Metric Spaces
 STOC'08
, 2008
"... In a multiarmed bandit problem, an online algorithm chooses from a set of strategies in a sequence of n trials so as to maximize the total payoff of the chosen strategies. While the performance of bandit algorithms with a small finite strategy set is quite well understood, bandit problems with larg ..."
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Cited by 45 (6 self)
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In a multiarmed bandit problem, an online algorithm chooses from a set of strategies in a sequence of n trials so as to maximize the total payoff of the chosen strategies. While the performance of bandit algorithms with a small finite strategy set is quite well understood, bandit problems with large strategy sets are still a topic of very active investigation, motivated by practical applications such as online auctions and web advertisement. The goal of such research is to identify broad and natural classes of strategy sets and payoff functions which enable the design of efficient solutions. In this work we study a very general setting for the multiarmed bandit problem in which the strategies form a metric space, and the payoff function satisfies a Lipschitz condition with respect to the metric. We refer to this problem as the Lipschitz MAB problem. We present a complete solution for the multiarmed problem in this setting. That is, for every metric space (L, X) we define an isometry invariant MaxMinCOV(X) which bounds from below the performance of Lipschitz MAB algorithms for X, and we present an algorithm which comes arbitrarily close to meeting this bound. Furthermore, our technique gives even better results for benign payoff functions.
Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design
"... Many applications require optimizing an unknown, noisy function that is expensive to evaluate. We formalize this task as a multiarmed bandit problem, where the payoff function is either sampled from a Gaussian process (GP) or has low RKHS norm. We resolve the important open problem of deriving regre ..."
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Cited by 45 (9 self)
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Many applications require optimizing an unknown, noisy function that is expensive to evaluate. We formalize this task as a multiarmed bandit problem, where the payoff function is either sampled from a Gaussian process (GP) or has low RKHS norm. We resolve the important open problem of deriving regret bounds for this setting, which imply novel convergence rates for GP optimization. We analyze GPUCB, an intuitive upperconfidence based algorithm, and bound its cumulative regret in terms of maximal information gain, establishing a novel connection between GP optimization and experimental design. Moreover, by bounding the latter in terms of operator spectra, we obtain explicit sublinear regret bounds for many commonly used covariance functions. In some important cases, our bounds have surprisingly weak dependence on the dimensionality. In our experiments on real sensor data, GPUCB compares favorably with other heuristical GP optimization approaches. 1.