Titlebook: Recent Advances in Reinforcement Learning; 8th European Worksho Sertan Girgin,Manuel Loth,Daniil Ryabko Conference proceedings 2008 Springe

宣誓書

,Policy Learning – A Unified Perspective with Applications in Robotics,umanoid robots. In this paper, we show two contributions: firstly, we show a unified perspective which allows us to derive several policy learning algorithms from a common point of view, i.e, policy gradient algorithms, natural-gradient algorithms and EM-like policy learning. Secondly, we present se

施魔法

FOIL

United We Stand: Population Based Methods for Solving Unknown POMDPs,cy, which is typically much simpler than the environment. We present a global search algorithm capable of finding good policies for POMDPs that are substantially larger than previously reported results. Our algorithm is general; we show it can be used with, and improves the performance of, existing

Mercantile

Regularized Fitted Q-Iteration: Application to Planning,ing a user-chosen kernel function. We derive bounds on the quality of the solution and argue that data-dependent penalties can lead to almost optimal performance. A simple example is used to illustrate the benefits of using a penalized procedure.

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Adherent

價值在貶值

0302-9743 reinfor- ment learning, on how it could be made more e?cient, applied to a broader range of applications, and utilized at more abstract and symbolic levels. As a participant in this 8th European Workshop on Reinforcement Learning, I was struck by both the quality and quantity of the presentations. T

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Efficient Reinforcement Learning in Parameterized Models: Discrete Parameter Case,nd for the algorithm is linear (up to a logarithmic term) in the size. of the parameter space, independently of the cardinality of the state and action spaces. We further demonstrate that much better dependence on . is possible, depending on the specific information structure of the problem.

finite

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Tile Coding Based on Hyperplane Tiles,on capabilities of the tile coding approximator: in the hyperplane tile coding broad generalizations over the problem space result only in a soft degradation of the performance, whereas in the usual tile coding they might dramatically affect the performance.