Hybridizing evolutionary computation and reinforcement learning for the design of almost universal controllers for autonomous robots

  • Authors:

  • Darío Maravall;Javier de Lope;José Antonio Martín H.

  • Affiliations:

  • Department of Artificial Intelligence, Universidad Politécnica de Madrid, Spain;Department of Applied Intelligent Systems, Universidad Politécnica de Madrid, Spain;Department de Sistemas Informáticos y Computación, Universidad Complutense de Madrid, Spain

  • Venue:
  • Neurocomputing
  • Year:
  • 2009

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Abstract

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.