Abstract
In this talk, I will present a framework for the automatic learning of control policies for nonlinear systems with continuous state spaces. The framework is based on the Ant Colony Optimization class of algorithms, which is inspired by the foraging behavior of ants. We have called our framework Ant Colony Learning (ACL). A collection of agents  called ants  jointly interact with the system at hand in order to find the optimal mapping between states and actions. Through the stigmergic interaction by  so called  pheromones, the ants are guided by each others experience towards better control policies. In order to deal with continuous state spaces, we establish a generalization of the concept of pheromones and the local and global pheromone update rules. As a result of this generalization, we can integrate both crisp and fuzzy partitioning of the state space into the ACL framework. We analyze the performance of ACL by applying it to the control problem of swingingup and stabilizing an underactuated pendulum.
Keywords
ant colony optimization, control policy learning, fuzzy interpolation
References

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Ant colony optimization for optimal control.
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Proceedings of the 2008 Congress on Evolutionary Computation (CEC 2008). pp. 20402046.

van Ast, J. M., Babuska, R., and De Schutter, B.. (2009)
Fuzzy ant colony optimization for optimal control.
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van Ast, J. M., Babuska, R., and De Schutter, B.. (2009)
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van Ast, J. M., Babuska, R., and De Schutter, B.. (2010)
Ant colony learning algorithm for optimal control.
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van Ast, J. M., Babuska, R., and De Schutter, B.. (2010)
Generalized pheromone update for ant colony learning in continuous state spaces.
In
Proceedings of the 2010 Congress on Evolutionary Computation (CEC 2010). Accepted for publication.