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156,785
Nonstationary policy learning in 2player zero sum games
 In Proc. AAAI Conference
, 2005
"... A key challenge in multiagent environments is the construction of agents that are able to learn while acting in the presence of other agents that are simultaneously learning and adapting. These domains require online learning methods without the benefit of repeated training examples, as well as the ..."
Abstract

Cited by 4 (1 self)
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algorithm, ELPH, based on a straightforward entropy pruning technique that is able to rapidly learn and adapt to nonstationary policies. We demonstrate the performance of this method in a nonstationary learning environment of adversarial zerosum matrix games.
Nonstationary Policy Learning in 2player Zero Sum Games
"... A key challenge in multiagent environments is the construction of agents that are able to learn while acting in the presence of other agents that are simultaneously learning and adapting. These domains require online learning methods without the benefit of repeated training examples, as well as the ..."
Abstract
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algorithm, ELPH, based on a entropy pruning technique that is able to rapidly learn and adapt nonstationary policies. We demonstrate the performance of this method in a nonstationary learning environment of adversarial zerosum matrix games.
Predicting How People Play Games: Reinforcement Learning . . .
 AMERICAN ECONOMIC REVIEW
, 1998
"... ..."
The Science of Monetary Policy: A New Keynesian Perspective
 Journal of Economic Literature
, 1999
"... “Having looked at monetary policy from both sides now, I can testify that central banking in practice is as much art as science. Nonetheless, while practicing this dark art, I have always found the science quEite useful.” 2 Alan S. Blinder ..."
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Cited by 1809 (45 self)
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“Having looked at monetary policy from both sides now, I can testify that central banking in practice is as much art as science. Nonetheless, while practicing this dark art, I have always found the science quEite useful.” 2 Alan S. Blinder
Rules, discretion, and reputation in a model of monetary policy
 JOURNAL OF MONETARY ECONOMICS
, 1983
"... In a discretionary regime the monetary authority can print more money and create more inflation than people expect. But, although these inflation surprises can have some benefits, they cannot arise systematically in equilibrium when people understand the policymakor's incentives and form their ..."
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Cited by 794 (9 self)
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In a discretionary regime the monetary authority can print more money and create more inflation than people expect. But, although these inflation surprises can have some benefits, they cannot arise systematically in equilibrium when people understand the policymakor's incentives and form their expectations accordingly. Because the policymaker has the power to create inflation shocks ex post, the equilibrium growth rates of money and prices turn out to be higher than otherwise. Therefore, enforced commitments (rules) for monetary behavior can improve matters. Given the repeated interaction between the policymaker and the private agents, it is possible that reputational forces can substitute for formal rules. Here, we develop an example of a reputational equilibrium where the outcomes turn out to be weighted averages of those from discretion and those from the ideal rule. In particular, the rates of inflation and monetary growth look more like those under discretion when the discount rate is high.
Boosting a Weak Learning Algorithm By Majority
, 1995
"... We present an algorithm for improving the accuracy of algorithms for learning binary concepts. The improvement is achieved by combining a large number of hypotheses, each of which is generated by training the given learning algorithm on a different set of examples. Our algorithm is based on ideas pr ..."
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Cited by 516 (15 self)
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We present an algorithm for improving the accuracy of algorithms for learning binary concepts. The improvement is achieved by combining a large number of hypotheses, each of which is generated by training the given learning algorithm on a different set of examples. Our algorithm is based on ideas
LeastSquares Policy Iteration
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2003
"... We propose a new approach to reinforcement learning for control problems which combines valuefunction approximation with linear architectures and approximate policy iteration. This new approach ..."
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Cited by 461 (12 self)
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We propose a new approach to reinforcement learning for control problems which combines valuefunction approximation with linear architectures and approximate policy iteration. This new approach
ERC  A Theory of Equity, Reciprocity and Competition
 FORTHCOMING AMERICAN ECONOMIC REVIEW
, 1999
"... We demonstrate that a simple model, constructed on the premise that people are motivated by both their pecuniary payoff and their relative payoff standing, explains behavior in a wide variety of laboratory games. Included are games where equity is thought to be a factor, such as ultimatum, twoperio ..."
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Cited by 699 (21 self)
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We demonstrate that a simple model, constructed on the premise that people are motivated by both their pecuniary payoff and their relative payoff standing, explains behavior in a wide variety of laboratory games. Included are games where equity is thought to be a factor, such as ultimatum, two
Results 1  10
of
156,785