Neuroevolutionary reinforcement learning for generalized helicopter control

Rogier Koppejan, Shimon Whiteson
2009 Proceedings of the 11th Annual conference on Genetic and evolutionary computation - GECCO '09  
Helicopter hovering is an important challenge problem in the field of reinforcement learning. This paper considers several neuroevolutionary approaches to discovering robust controllers for a generalized version of the problem used in the 2008 Reinforcement Learning Competition, in which wind in the helicopter's environment varies from run to run. We present the simple model-free strategy that won first place in the competition and also describe several more complex model-based approaches. Our
more » ... mpirical results demonstrate that neuroevolution is effective at optimizing the weights of multi-layer perceptrons, that linear regression is faster and more effective than evolution for learning models, and that model-based approaches can outperform the simple modelfree strategy, especially if prior knowledge is used to aid model learning.
doi:10.1145/1569901.1569922 dblp:conf/gecco/KoppejanW09 fatcat:sml4esoqtveabcpba56peklf6m