Evolving neural network ensembles for control problems

In neuroevolution, a genetic algorithm is used to evolve a neural network to perform a particular task. The standard approach is to evolve a population over a number of generations, and then select the final generation's champion as the end result. However, it is possible that there is valuable information present in the population that is not captured by the champion. The standard approach ignores all such information. One possible solution to this problem is to combine multiple individuals from the final population into an ensemble. This approach has been successful in supervised classification tasks, and in this paper, it is extended to evolutionary reinforcement learning in control problems. The method is evaluated on a challenging extension of the classic pole balancing task, demonstrating that an ensemble can achieve significantly better performance than the champion alone.

[1]  Anders Krogh,et al.  Neural Network Ensembles, Cross Validation, and Active Learning , 1994, NIPS.

[2]  Geoffrey E. Hinton,et al.  Adaptive Mixtures of Local Experts , 1991, Neural Computation.

[3]  David W. Opitz,et al.  Generating Accurate and Diverse Members of a Neural-Network Ensemble , 1995, NIPS.

[4]  Xin Yao,et al.  Towards Designing Neural Network Ensembles by Evolution , 1998, PPSN.

[5]  César Hervás-Martínez,et al.  Cooperative coevolution of artificial neural network ensembles for pattern classification , 2005, IEEE Transactions on Evolutionary Computation.

[6]  Xin Yao,et al.  Evolving artificial neural networks , 1999, Proc. IEEE.

[7]  Risto Miikkulainen,et al.  Evolving populations of expert neural networks , 2001 .

[8]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[9]  Risto Miikkulainen,et al.  Evolving Neural Networks through Augmenting Topologies , 2002, Evolutionary Computation.

[10]  Risto Miikkulainen,et al.  2-D Pole Balancing with Recurrent Evolutionary Networks , 1998 .

[11]  Xin Yao,et al.  Making use of population information in evolutionary artificial neural networks , 1998, IEEE Trans. Syst. Man Cybern. Part B.

[12]  J. D. Schaffer,et al.  Combinations of genetic algorithms and neural networks: a survey of the state of the art , 1992, [Proceedings] COGANN-92: International Workshop on Combinations of Genetic Algorithms and Neural Networks.

[13]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[14]  Lars Kai Hansen,et al.  Neural Network Ensembles , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Sridhar Mahadevan,et al.  Recent Advances in Hierarchical Reinforcement Learning , 2003, Discret. Event Dyn. Syst..