@article {IOPORT.05718885,
    author       = {Maravall, Dar{\'\i}o and de Lope, Javier and H., Jos\'e Antonio Mart{\'\i}n},
    title        = {Hybridizing evolutionary computation and reinforcement learning for the design of almost universal controllers for autonomous robots.},
    year         = {2009},
    journal      = {Neurocomputing},
    volume       = {72},
    number       = {4-6},
    issn         = {0925-2312},
    pages        = {887-894},
    publisher    = {Elsevier Science Publishers B.V., Amsterdam},
    doi          = {10.1016/j.neucom.2008.04.058},
    abstract     = {Summary: In this paper a hybrid approach to the autonomous motion control of robots in cluttered environments with unknown obstacles is introduced. It is shown the efficiency of a hybrid solution by combining the optimization power of evolutionary algorithms and at the same time the efficiency of reinforcement learning in real-time and on-line situations. Experimental results concerning the navigation of a L-shaped robot in a cluttered environment with unknown obstacles are also presented. In such environments there appear real-time and on-line constraints well-suited to RL algorithms and, at the same time, there exists an extremely high dimension of the state space usually unpractical for RL algorithms but well-suited to evolutionary algorithms. The experimental results confirm the validity of the hybrid approach to solve hard real-time, on-line and high dimensional robot motion planning and control problems, where the RL approach shows some difficulties.},
    identifier   = {05718885},
}