Technical Note: \cal Q-Learning
Machine Learning
Building agents for service provisioning out of components
Proceedings of the fifth international conference on Autonomous agents
Interaction graphs for planning problem decomposition
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Advanced Grid Management Software for Seamless Services
Multiagent and Grid Systems - Smart Grid Technologies & Market Models
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The automatic computation of an optimal solution for a complex problem is a challenging task if no additional knowledge is available. For bounded sized problems there are universally applicable algorithms (e.g. genetic algorithms, branch and bound, reinforcement learning). The disadvantage of these algorithms is their high computational complexity so that real world problems can only be solved efficiently, if the search space is reduced dramatically. In this paper we present an approach that enables the automatic computation of the parameter dependencies of a complex problem without any additional information. The basic idea is to apply reinforcement learning and to incrementally acquire knowledge about the implicit parameters dependencies. Based on the obtained data an optimal strategy is learned. For speeding up the learning process a multiagent architecture is applied, that supports the simultaneous analysis of alternative strategies. We prove the advantages of our approach by successfully learning a control strategy for a model helicopter.